{"as_of":"2026-08-08T07:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dfde51468a11a9a8c4a3034f58e7436d5350c5dc3d8f7efc7b3e4a0151ed83d4","coverage":[{"denominator":127,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:38:09.530568Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.10142/citation-record","integrity":"/paper/2506.10142/integrity","json":"/paper/2506.10142/citation-record.json","paper":"/paper/2506.10142"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:05.921358Z","title":"Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:05.921358Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:bc5a3659eaf45773f13d0260088904d3770adb5ab94fcc7fbcaf771914eddb59","observation_id":"304f034f-919f-40b7-b7a3-910005448904","resolution":{"observed_at":"2026-08-07T04:38:05.921358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:05.963527Z","title":"The multimodal brain tumor image segmentation benchmark (brats),","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:05.963527Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:f937228b5559d0710dc2e8261b1ffbf0c20ec27d6229cffe012bfbf882868a1e","observation_id":"657804df-0733-46cd-9dd4-58bca1c0adee","resolution":{"observed_at":"2026-08-07T04:38:05.963527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.007572Z","title":"Brain tumor seg- mentation using convolutional neural networks in mri images,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.007572Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:eb9711c163e406d2bb96eccc0bbcb6e60c8677fbc8dad9eee837e2e02399fefd","observation_id":"42d79106-1f26-47d5-9a70-b6b364bf3ec2","resolution":{"observed_at":"2026-08-07T04:38:06.007572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.090835Z","title":"Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.090835Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:6af491f654705d4807e520afb6e7264e393db33cdf278ee6180fd4d776ef8b97","observation_id":"fa890df0-aed6-42eb-a198-4768d939dd1a","resolution":{"observed_at":"2026-08-07T04:38:06.090835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.163195Z","title":"Patterns of tumor contrast enhancement predict the prognosis of anaplastic gliomas with idh1 mutation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.163195Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:091f0de0cfa4d4fd1b4377ac26b660bfd4da45e1ce403fbdfd14783dde1ccf97","observation_id":"b32592e3-75cb-4ba4-bfc1-b00d37707857","resolution":{"observed_at":"2026-08-07T04:38:06.163195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.246056Z","title":"Mri features predict p53 status in lower-grade gliomas via a machine-learning approach,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.246056Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:f07f6d36951c39f5b13e611a3b1991cdd612fa8b92fa22f3ac55ba00033fcbd0","observation_id":"58a098d5-16b9-4e0e-9e4d-3727f589da59","resolution":{"observed_at":"2026-08-07T04:38:06.246056Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.285335Z","title":"Texture analysis in brain tumor mr imaging,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.285335Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:72f44a2d2cbc98dadec1a9c7f56a80eeef0202d1af91bf776cea69688fdf0449","observation_id":"ef18dd40-a41b-4548-a4d5-12ce8546fafe","resolution":{"observed_at":"2026-08-07T04:38:06.285335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.331974Z","title":"Texture analysis: a review of neuro- logic mr imaging applications,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.331974Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:ea250e988a486631885bd2280bb9d8389c74e6635314f591dbe9ec36ece48825","observation_id":"4b2aacfe-c8c1-4ea6-a83a-29b61dbbe352","resolution":{"observed_at":"2026-08-07T04:38:06.331974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.395181Z","title":"Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.395181Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:59dacc4c4c6259d20bbc9e8bb13cf46e8f459a7f8d5960b7c9e4fa217ac4f7da","observation_id":"73109bde-a97a-40bb-94ef-57bab7b1c090","resolution":{"observed_at":"2026-08-07T04:38:06.395181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.464634Z","title":"Differentiating high- grade gliomas from brain metastases at magnetic resonance: the role of texture analysis of the peritumoral zone,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.464634Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:dea74e94aee60703d823ae340bfab311bdc0ef2d90397c1369c33487c998692a","observation_id":"2a904df8-7de4-43bc-b16f-1b3cd3db460b","resolution":{"observed_at":"2026-08-07T04:38:06.464634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.527019Z","title":"Quantitative metric for mr brain tumour grade classification using sample space density measure of analytic intrinsic mode function representation,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.527019Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:0cee4fb97f1cf8552b445f14c1e261ad4f340aba3639e86118f695a538125891","observation_id":"0198061d-87a3-4da2-afe7-1d7b354575d8","resolution":{"observed_at":"2026-08-07T04:38:06.527019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.573906Z","title":"Classification and segmentation of brain tumor using texture analysis,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.573906Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:920818e47b7fccafa22e9a71a58872565a70f4690eea6a98094316be5f4d10c8","observation_id":"5023e32e-e3dc-4b6a-b0b2-4103b5159a9a","resolution":{"observed_at":"2026-08-07T04:38:06.573906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.613273Z","title":"Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.613273Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:4b7e2415ccb5858bb8d47d948bbe55b3e6bff1b4a1313b139609527d64070e25","observation_id":"67e43ac4-16f0-411c-bb75-ce06111a1cce","resolution":{"observed_at":"2026-08-07T04:38:06.613273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.664594Z","title":"Texture analysis in cerebral gliomas: a review of the literature,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.664594Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:1843211b0cdbc0f39bbc96a105ab47224b5c0ce94b359d8ccfcb3ccbf6475d6a","observation_id":"33f272c5-72a6-4c2a-879d-f37e7e921a61","resolution":{"observed_at":"2026-08-07T04:38:06.664594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.731849Z","title":"Assessment of multiphasic contrast-enhanced mr textures in differentiating small renal mass subtypes,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.731849Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:555fb188a5efeaabe39258ec77f2a62d30901ae4dca1d5abd566c121c98c8682","observation_id":"8b5240b4-a6ca-4255-90de-4b5dd197cb73","resolution":{"observed_at":"2026-08-07T04:38:06.731849Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.755989Z","title":"Diagnostic performance of texture analysis on mri in grading cerebral gliomas,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.755989Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:96c13715b48fdd6eabca8c40af30b1bd18c68a76a73bc760411e31562dc6b479","observation_id":"1c5045ae-a979-438d-87e1-7a2e0d33735e","resolution":{"observed_at":"2026-08-07T04:38:06.755989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.840450Z","title":"Characterizing brain tumor regions using texture analysis in magnetic resonance imaging,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.840450Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:f3c083b9c140aaad5e8a98573bb3254132d7b5d64eace2c0b0b01bbb9110891c","observation_id":"f883ef69-efdb-4475-b593-52c1111e2d79","resolution":{"observed_at":"2026-08-07T04:38:06.840450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.886455Z","title":"A survey on deep learning in medical image analysis,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.886455Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e75b5dac8c3c4f3126ed10b13c0770fabdc3c4809803470f383278e414fa41ba","observation_id":"1d6f3c68-2cb9-4f00-9930-a326b8aa1e11","resolution":{"observed_at":"2026-08-07T04:38:06.886455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.917806Z","title":"Fda: Fourier domain adaptation for semantic segmentation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.917806Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:d2a22bc852d381c1c24d1334dc27d30972c4af530acecc8c43d977cf41c98db9","observation_id":"54d27548-1362-46da-8ad2-e9fa99b84745","resolution":{"observed_at":"2026-08-07T04:38:06.917806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:06.962787Z","title":"Deep learning based brain tumor segmentation: a survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:06.962787Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:15c1bd3382edd2c8867b3eb88ea53568941f08269714f1fc2b1e42c02332baec","observation_id":"74dffa3a-b3ca-42d4-9f9a-82a62341c56a","resolution":{"observed_at":"2026-08-07T04:38:06.962787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.046437Z","title":"Brain tumor target volume determination for radiation treatment planning through automated mri segmentation,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.046437Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a1ce806e5b37e631c0eb29a28b171d204e672f612ac2d6643e0fde31f18836b9","observation_id":"1f2c3305-ba6d-4835-87f5-64fa9a52ab99","resolution":{"observed_at":"2026-08-07T04:38:07.046437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.074142Z","title":"Baseline pretreatment contrast enhancing tumor volume including cen- tral necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.074142Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:806b294d8ac95ac1f34ae65a82e90373ccdc2b603658311bc6f5119d04782277","observation_id":"d35ab58c-b556-483a-a774-00e48520ca5e","resolution":{"observed_at":"2026-08-07T04:38:07.074142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.107272Z","title":"Com- parison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.107272Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e750bd110f8fbfd785a7b54c71f6049a6a8ae0fdbfb151cc92213515be7381fd","observation_id":"f3c32911-f29d-4711-9172-51e6fcb982b8","resolution":{"observed_at":"2026-08-07T04:38:07.107272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.185924Z","title":"Medical image segmentation based on frequency domain decomposition svd linear attention,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.185924Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:be81b2043b93bf74b4f3d2c0e7f3b4ee6cd82cd091d2e27f626a4f1dae140d13","observation_id":"b9dc4a09-2419-46fb-bc94-50a54725ed3f","resolution":{"observed_at":"2026-08-07T04:38:07.185924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.234397Z","title":"Prior wavelet knowledge for multi-modal medical image segmentation using a lightweight neural network with attention guided features,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.234397Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:ac8ed8e4236e3f6bad10bb758b23bb9dcbfca46d67efae38e33d81de93d46d6e","observation_id":"a0883a8b-498c-4574-8895-e5e04ab780cb","resolution":{"observed_at":"2026-08-07T04:38:07.234397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09216","last_updated":"2024-09-13T22:10:14Z","snapshot_observed_at":"2026-08-07T09:05:15.785254Z","submitted_at":"2024-09-13T22:10:14Z","title":"Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition","version":1},"cited_work":{"arxiv_id":"2409.09216","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.09216","snapshot_observed_at":"2026-08-07T04:38:09.825712Z","title":"Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition","venue":"eess.IV","work_id":"3455a7d9-1784-469a-b64f-050ca4118e65","year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.286551Z"},"links":{"cited_paper":"/paper/2409.09216","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:01438c3a8916b0b8d73dccd38bed085c14a4441afab8b173c75639301e837510","observation_id":"cfc408c1-9280-4b60-9d53-20bf24fc4438","resolution":{"observed_at":"2026-08-07T04:38:09.828904Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.368841Z","title":"Dual-tree complex wavelet pooling and attention-based modified u-net architecture for automated breast thermogram segmentation and classification,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.368841Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a055a254fca00ed8e17e3f153f0694506c050d9c43852a0f646ce9f57be816c7","observation_id":"f428d09d-5566-4167-9167-7c549b234a21","resolution":{"observed_at":"2026-08-07T04:38:07.368841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.430801Z","title":"Optimal deep learning architecture for automated segmentation of cysts in oct images using x-let transforms,","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.430801Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:fd271d229b843260c9a23317c7942a363763727b4d457e27eba3f4c67cf0c50d","observation_id":"2cc98b12-2e83-4d9a-b982-31e23483a32b","resolution":{"observed_at":"2026-08-07T04:38:07.430801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.481577Z","title":"Wavelet u-net++ for accurate lung nodule segmentation in ct scans: Improving early detection and diagnosis of lung cancer,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.481577Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:6c95b292a9393acc233995412b84672db8c220a98f8c67fe63f0e2b8981e0679","observation_id":"446d9ca8-987e-49cb-b9eb-94218941b428","resolution":{"observed_at":"2026-08-07T04:38:07.481577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.515206Z","title":"Wranet: wavelet integrated residual attention u-net network for medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.515206Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:87e0916114ee07a193a69fcf0379f81f337ccf3f229f07503cc68895ed30e987","observation_id":"543679ab-85bb-4d81-82a0-7921a5845b45","resolution":{"observed_at":"2026-08-07T04:38:07.515206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.561364Z","title":"A dual-tree complex wavelet transform based convolutional neural network for hu- man thyroid medical image segmentation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.561364Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:0e192f67e0a5c95134a77ed3491a1e084ade79f73d65bf689f82451eeb158931","observation_id":"93eb6486-c680-4ca6-a2d0-9baeac72f735","resolution":{"observed_at":"2026-08-07T04:38:07.561364Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.600413Z","title":"Medical im- age fusion based on convolutional neural networks and non-subsampled contourlet transform,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.600413Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:d51907daef5d0a34a8b0f6d92bcd3b91c0b6f25fb721841b6ee089bbe26265d4","observation_id":"4f8a1052-685c-42b0-9c57-596eb9d2abfb","resolution":{"observed_at":"2026-08-07T04:38:07.600413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.641489Z","title":"A deep transfer learning based architecture for brain tumor classification using mr images,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.641489Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:650c242462f5740362039b4e437ade691d3df8307870b64822173c4609dbf63b","observation_id":"cb55a300-e76e-45cf-b674-d7d787576867","resolution":{"observed_at":"2026-08-07T04:38:07.641489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.671454Z","title":"Brain tumor classification using meta-heuristic optimized convolutional neural networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.671454Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:b11d3d43b1df96f3cbd8bacc9443c14d4d75a6092aa47895b67c5747556344b8","observation_id":"694511e5-b196-4c6e-8121-85767cfa5175","resolution":{"observed_at":"2026-08-07T04:38:07.671454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.730431Z","title":"Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.730431Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:f99ae72c54d106c3c1e3812066606393edbcde72053ab2d4bff8ada145fb08c9","observation_id":"5bdd4c34-4ee1-4b4a-baa8-ae7e7e091871","resolution":{"observed_at":"2026-08-07T04:38:07.730431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.776031Z","title":"An efficient approach for the detection of brain tumor using fuzzy logic and u-net cnn classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.776031Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:7b54457de3a61b8cd420151e164c5bc005fd088fb517689954d4cf497d292dd6","observation_id":"8dd89ba0-31cd-4bc4-8874-e139768c8f18","resolution":{"observed_at":"2026-08-07T04:38:07.776031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.807136Z","title":"Glioma/glioblastoma detection in brain mri using pre-trained deep-learning scheme,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.807136Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:c3f68f69b504357b70795242c37d4c6774fa66db50740dd11f67a5f06b056ad3","observation_id":"13432fe8-b2d8-460f-9ec6-08f00502e170","resolution":{"observed_at":"2026-08-07T04:38:07.807136Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.854600Z","title":"Dsleepnet: Disentanglement learning for personal attribute-agnostic three-stage sleep classification using wearable sensing data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.854600Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:8f9c7dcde7933314da508bd0274d4fb1cd1033b87ba299ec5ec331e294755aee","observation_id":"2128fdee-65bc-41c8-9c26-517cd59af642","resolution":{"observed_at":"2026-08-07T04:38:07.854600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.906901Z","title":"Sid-nerf: Few-shot nerf based on scene information distribution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.906901Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a9fea87ff8cf520bd48eabbd4057bd6b2b342e5beae15b25b456dea5cb62d66b","observation_id":"2a3b8458-b823-400c-8142-a1871528e5da","resolution":{"observed_at":"2026-08-07T04:38:07.906901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.942232Z","title":"Depth-aware endo- scopic video inpainting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.942232Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:d18fff7c8dfa8baf3704155ad6b07b4eaba80565e9617d440b10e16122d416df","observation_id":"379d6823-8ffd-471a-bfb6-7b1d25e1a68a","resolution":{"observed_at":"2026-08-07T04:38:07.942232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:07.984013Z","title":"Rules for expectation: Learning to generate rules via social environment modeling,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:07.984013Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e812e21a8fd58595f4b0dc181ac086547b83766034be83a8f363c72287f4749e","observation_id":"d74d71fb-d290-45f9-8404-21370061ba27","resolution":{"observed_at":"2026-08-07T04:38:07.984013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.024988Z","title":"Sentinel- guided zero-shot learning: A collaborative paradigm without real data exposure,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.024988Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:041ade690dffd1db3f9df2eca0b4bb410ae5eefc7cc403588857a644fe60beb3","observation_id":"3161d9a1-a420-432c-929a-c9efb5cf7751","resolution":{"observed_at":"2026-08-07T04:38:08.024988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.01888","last_updated":"2025-05-03T18:40:39Z","snapshot_observed_at":"2026-08-07T15:56:44.634934Z","submitted_at":"2025-05-03T18:40:39Z","title":"Rethinking Score Distilling Sampling for 3D Editing and Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.01888","snapshot_observed_at":"2026-08-07T04:38:08.087795Z","title":"Rethinking score dis- tilling sampling for 3d editing and generation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.087795Z"},"links":{"cited_paper":"/paper/2505.01888","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:cbf13523a33cefb3df085cf15dc17ffd7c5db5933521a80d6a1da4e3f10bda45","observation_id":"96eb1c3e-3e7d-4377-9532-94286c82d06e","resolution":{"observed_at":"2026-08-07T04:38:08.087795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.130434Z","title":"Laser: Efficient language-guided segmentation in neural radiance fields,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.130434Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:714eae5de5aea1117bf279b0b2d8cbe65357b56ef400b0f99118c5d9fa5695ef","observation_id":"15d014f3-9cb6-4942-8d8a-6488737d39c1","resolution":{"observed_at":"2026-08-07T04:38:08.130434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.198621Z","title":"Dynamic unary convolution in transformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.198621Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:5507bd21b3a79e1697d49d5241ee256c9746b1aa5632a10df8f740f3400fec9f","observation_id":"14552371-5c71-46ae-a9d0-ef9ecc1e6767","resolution":{"observed_at":"2026-08-07T04:38:08.198621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.263295Z","title":"Parameter efficient fine-tuning for multi-modal generative vision models with m¨obius-inspired transformation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.263295Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:63a0c15ad507cbd7e215c94f146153fd05da3d0267f1372c9f67dee4c4d99925","observation_id":"2cf57fe5-3830-4276-8166-19806318a545","resolution":{"observed_at":"2026-08-07T04:38:08.263295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.309182Z","title":"Unified spatial-temporal edge-enhanced graph networks for pedestrian trajec- tory prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.309182Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e62a66716a2c6722eb60206dec3c6a8cbdb0fc750acded8cd5e12df25e23f25b","observation_id":"e9c3155b-48e8-4ba2-a8ee-640d1efb8fd7","resolution":{"observed_at":"2026-08-07T04:38:08.309182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.351458Z","title":"Bp-sgcn: Be- havioral pseudo-label informed sparse graph convolution network for pedestrian and heterogeneous trajectory prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.351458Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:d58a6db5c3eaa0b8e8e41e9f5baf7d74a7c9026cc080002af9f152f69095392d","observation_id":"b62619e4-29e3-4334-8e99-004018b8995e","resolution":{"observed_at":"2026-08-07T04:38:08.351458Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04542","last_updated":"2025-05-30T15:09:49Z","snapshot_observed_at":"2026-07-06T15:39:49.273453Z","submitted_at":"2023-06-07T15:46:47Z","title":"On the Design Fundamentals of Diffusion Models: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.04542","snapshot_observed_at":"2026-08-07T04:38:08.385443Z","title":"On the design fundamen- tals of diffusion models: A survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.385443Z"},"links":{"cited_paper":"/paper/2306.04542","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:36eb1ca861500b268770e3fcdd8ac209672ba67798190c163b4c0deaa93b3f7a","observation_id":"1e0de972-0b51-484d-ab9d-ef9a6b8bbbb4","resolution":{"observed_at":"2026-08-07T04:38:08.385443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.430516Z","title":"Hint: High- quality inpainting transformer with mask-aware encoding and enhanced attention,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.430516Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a51caa0a7dcb5a2a7a06af8b9dd7a7cf2539ccc4fd05960e4193cb33019309c5","observation_id":"08dd154c-a0d4-4712-9096-e1ecfa873280","resolution":{"observed_at":"2026-08-07T04:38:08.430516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.357749Z","title":"3d u-net: learning dense volumetric segmentation from sparse annotation,","venue":null,"work_id":"618e4ed1-a834-4cce-ae0d-32f58e365bfb","year":2016},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.497641Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:386d453e46f72a58e87c8c3fb747d9db15a2466dc57fe8463841f3079ce86e21","observation_id":"471a7f66-ffe2-4bad-9236-961e7ae2cf7c","resolution":{"observed_at":"2026-08-07T04:38:10.362305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.528717Z","title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.528717Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:c6150be4137bc8e58ec666b1a220ae81dd8105d02c190abb106a7892371ce2f5","observation_id":"12e37b74-f0cb-48b1-a14c-17e576e7cca0","resolution":{"observed_at":"2026-08-07T04:38:08.528717Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.344182Z","title":"Unetr: Transformers for 3d medical image segmentation,","venue":null,"work_id":"d4b5c2a0-02d3-4d93-bb55-ce8058081c9b","year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.571234Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:44134fce28350fea2bd6caf1e0ec6a0e6156e88156ed9e0fe5a62a62c248af79","observation_id":"10622fbf-4b88-4ede-a9fe-9757d9967366","resolution":{"observed_at":"2026-08-07T04:38:10.347287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03999","last_updated":"2018-05-20T23:33:30Z","snapshot_observed_at":"2026-07-06T06:32:53.966022Z","submitted_at":"2018-04-11T14:13:03Z","title":"Attention U-Net: Learning Where to Look for the Pancreas","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03999","snapshot_observed_at":"2026-08-07T04:38:08.631748Z","title":"Atten- tion u-net: Learning where to look for the pancreas,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.631748Z"},"links":{"cited_paper":"/paper/1804.03999","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:658b99df0c73e45dcdc421e43e36644ce122e931493a6714b7119d47bcd3a885","observation_id":"05e9662b-c3a8-40a2-a70c-86024261da84","resolution":{"observed_at":"2026-08-07T04:38:08.631748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:08.665504Z","title":"Road extraction by deep residual u-net,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.665504Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:dbcf99bbb002fbca1239beab03bb34e950b5950fac1429a47d85d3674d253576","observation_id":"65e8d082-e5f4-40d1-a734-653c573baef3","resolution":{"observed_at":"2026-08-07T04:38:08.665504Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.328898Z","title":"Unetr++: delving into efficient and accurate 3d medical image segmentation,","venue":null,"work_id":"ae0ee047-8b77-45d1-ac76-803fbc61789f","year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.718984Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:5fce9fc9f5bba3646f443a842376c7ad0893c8a1e10b61671f39fe71de6360d8","observation_id":"0f295dc9-05db-4a78-a96d-5b5b60b006f2","resolution":{"observed_at":"2026-08-07T04:38:10.332916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.319472Z","title":"Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task,","venue":null,"work_id":"86458bc4-7c97-4d0e-844a-16da1d4ae480","year":2019},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.793687Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:6776a8cf83fbe6df8531d9dbe13347d4f4f625cc480a6311511de116d26df29b","observation_id":"a17cbbc8-6a06-4a93-8669-e0d21db5fbe7","resolution":{"observed_at":"2026-08-07T04:38:10.322452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.311339Z","title":"Sgeresu- net for brain tumor segmentation,","venue":null,"work_id":"b40a6815-903d-46e0-82c3-8f2393bfe7c6","year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.852607Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:ad5ee67e7047f160cc5dc36893df61e781738e7a3385928c435d628c00ef744a","observation_id":"c0b852b5-2140-48f1-89bd-0e1e2304af4b","resolution":{"observed_at":"2026-08-07T04:38:10.314405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.301864Z","title":"Modality-adaptive feature interaction for brain tumor segmentation with missing modalities,","venue":null,"work_id":"16827920-7971-426d-bc9d-c55b5e97114b","year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.938338Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:ed2e681fd3efe0c08447a96cfb5b1f36d3d02af9db62822db88d4ba68d38a69c","observation_id":"34853e17-1ca3-4cfc-a1a7-f0d5190c0050","resolution":{"observed_at":"2026-08-07T04:38:10.305774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04306","last_updated":"2021-02-08T16:10:50Z","snapshot_observed_at":"2026-07-06T10:39:29.945712Z","submitted_at":"2021-02-08T16:10:50Z","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04306","snapshot_observed_at":"2026-08-07T04:38:08.985990Z","title":"Transunet: Transformers make strong encoders for medical image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:08.985990Z"},"links":{"cited_paper":"/paper/2102.04306","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:affd0edbc0321e3260c914e835e57b37ae0ddc6f239013c5a074f5a14201e068","observation_id":"3f203293-4dc0-4b68-b8ab-c6e588a55f1e","resolution":{"observed_at":"2026-08-07T04:38:08.985990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-07T04:38:09.047934Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.047934Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a85c14871f7d9133d84b00e85f95f52a2346f65d8ab1951e1a8e9de291d1268d","observation_id":"25c19f64-d482-44cf-83c0-4bbc06c2a857","resolution":{"observed_at":"2026-08-07T04:38:09.047934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.291891Z","title":"Hnf-netv2 for brain tumor segmentation using multi-modal mr imaging,","venue":null,"work_id":"07632c38-f008-41c7-8a0a-0715a1a3806f","year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.130941Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:30c8bcef6fb76ff9c51861f5eb049cf0377f798507ce15e92f1a0881eb8c348d","observation_id":"93d82cbe-21a7-431b-8938-20fa96c55339","resolution":{"observed_at":"2026-08-07T04:38:10.294512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.282764Z","title":"Sa-lut-nets: learning sample-adaptive intensity lookup tables for brain tumor segmentation,","venue":null,"work_id":"d16d0d4d-96f1-4759-ae73-a04da8a74103","year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.165546Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:c4e7a37814e637c3efa5f34026aaf41cc989a0d3cdb40eb38095d52486e5d76f","observation_id":"118d2909-c6a2-4cc6-a75e-f676d5697ac4","resolution":{"observed_at":"2026-08-07T04:38:10.285978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.273392Z","title":"Medical im- age segmentation via single-source domain generalization with random amplitude spectrum synthesis,","venue":null,"work_id":"fc37ea60-ba12-490c-8641-bae180c5ec36","year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.282541Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:8829360f53bb67804266df70450dcaa208755c9912a72cd2f779d7f57af62003","observation_id":"5f8d69e3-f0b8-472e-8d31-7f8982c68eb9","resolution":{"observed_at":"2026-08-07T04:38:10.276450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.265409Z","title":"A review on brain tumor segmentation based on deep learning methods with federated learning techniques,","venue":null,"work_id":"35c05f8b-44af-48d8-92a9-f6a30460f44f","year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.321221Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:343ef50e3f82b09a75c67278a0769e22b7d06adbae41908b98b94d2dd6a89326","observation_id":"5d03e33a-97b5-44b6-a2f4-0f5976ab7799","resolution":{"observed_at":"2026-08-07T04:38:10.268225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.257112Z","title":"Innovative multi-class segmenta- tion for brain tumor mri using noise diffusion probability models and enhancing tumor boundary recognition,","venue":null,"work_id":"0d8078f7-b751-44e2-95eb-ae2b065a174b","year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.380260Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e14f107819426f9e5c0ac32b515e49650a6538dfb51caa1d7949b7cd7071d01d","observation_id":"ab121a0d-7bc7-45ba-8d4f-33831ea23a99","resolution":{"observed_at":"2026-08-07T04:38:10.260083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:09.442320Z","title":"Learning in the frequency domain,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.442320Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:fc306f2b46f5599383e789be36516d674f2f451a9da97706e1b7a0ab44d0921d","observation_id":"e011c64d-6fd9-4404-80de-a3873b37752b","resolution":{"observed_at":"2026-08-07T04:38:09.442320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.242032Z","title":"Discrete cosin trans- former: Image modeling from frequency domain,","venue":null,"work_id":"eda36c88-db11-434c-8037-cf61782adc77","year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.444945Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:cdc3ee5474f9d7c3f742f7e0f13ea70a999d7dbb149bc81ce4e67fd285cb0365","observation_id":"85301623-ebb1-4925-98b1-721c6109991a","resolution":{"observed_at":"2026-08-07T04:38:10.244734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14302","last_updated":"2024-06-09T03:32:58Z","snapshot_observed_at":"2026-07-06T14:56:35.804487Z","submitted_at":"2023-02-28T04:31:09Z","title":"Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain","version":3},"cited_work":{"arxiv_id":"2302.14302","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.14302","snapshot_observed_at":"2026-08-07T04:38:09.683201Z","title":"Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain","venue":"cs.CV","work_id":"07ea784c-5783-4c0c-968e-f5f995d3f297","year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.447343Z"},"links":{"cited_paper":"/paper/2302.14302","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:5a68a81577ab9a2027b840afdcf3020d91cfd5eeccd20b060c83f36679b6c57d","observation_id":"a19db576-cd9e-4a68-9075-b91c19b33c0a","resolution":{"observed_at":"2026-08-07T04:38:09.686754Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18616","last_updated":"2024-05-28T21:45:46Z","snapshot_observed_at":"2026-08-01T17:55:00.348673Z","submitted_at":"2024-05-28T21:45:46Z","title":"Wavelet-Based Image Tokenizer for Vision Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18616","snapshot_observed_at":"2026-08-07T04:38:09.450154Z","title":"Wavelet-based image tokenizer for vision transformers,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.450154Z"},"links":{"cited_paper":"/paper/2405.18616","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a950dcb6af880709f2c3d7c1ce8c34a83105460cd922392d744093879df4a000","observation_id":"edfc23b5-104a-4868-a158-1227fd91294c","resolution":{"observed_at":"2026-08-07T04:38:09.450154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.232329Z","title":"Focal frequency loss for image reconstruction and synthesis,","venue":null,"work_id":"b6cf2bed-97c9-40f0-8e5b-b9a8ba2ede2b","year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.452914Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:305ec101e35fe2928f0672e0c322300cf800e1a69ecf5e32866580a7787d8187","observation_id":"45fb8f97-67e1-4572-806f-0bbcdf9f82b5","resolution":{"observed_at":"2026-08-07T04:38:10.236260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.223361Z","title":"Wavelet diffusion models are fast and scalable image generators,","venue":null,"work_id":"41bf8417-a952-4012-979e-b18a6ce2fdd9","year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.455282Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e102f1eb3b381a8c4371341402683a06a9f2ee3c3b9f4439183b29cb42dd7b2d","observation_id":"991563e6-d17b-497a-8e14-cd9f8b8f839c","resolution":{"observed_at":"2026-08-07T04:38:10.226581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:09.457908Z","title":"Fourier space losses for efficient perceptual image super-resolution,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.457908Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:549247afe4b1af182194db49c87655bc1bf0134a47c37a82c0654f32119beb67","observation_id":"90a3565b-912c-4b0f-a1ee-5775ef8f379b","resolution":{"observed_at":"2026-08-07T04:38:09.457908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.210032Z","title":"Spectral bayesian uncertainty for image super-resolution,","venue":null,"work_id":"57c1ed82-a529-4d9d-9ecb-b3e6ad5326f9","year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.460787Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:18e80e875b0dde2c357d7e38192c5ea90f37f2aeeaf672c6a5d17eef7f8f9ead","observation_id":"910f2eee-9dfe-4da2-a61c-30479d69a63a","resolution":{"observed_at":"2026-08-07T04:38:10.213036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.201787Z","title":"Sea ice change de- tection in sar images based on convolutional-wavelet neural networks,","venue":null,"work_id":"69214733-2909-4924-8f97-92297768d19a","year":2019},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.463247Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:6f60deb25ceb990f54e773b658d4a0107f6e87d61a365e7f70d5bae009cbb330","observation_id":"ac7f681c-7a64-4e03-a5fd-a494cb58a7b3","resolution":{"observed_at":"2026-08-07T04:38:10.205065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.193791Z","title":"Xnet: Wavelet- based low and high frequency fusion networks for fully-and semi- supervised semantic segmentation of biomedical images,","venue":null,"work_id":"4cce93c7-8e1c-4835-b46b-4ceffe782d96","year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.465714Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:454faec952caa1136a3e7f60a629400fe2cd53c168ef7ffcc64a2b952d2d9c6f","observation_id":"468bacfe-9d18-445b-b207-55d4738a9dec","resolution":{"observed_at":"2026-08-07T04:38:10.196815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.185693Z","title":"Aerial lanenet: Lane-marking semantic segmentation in aerial imagery using wavelet- enhanced cost-sensitive symmetric fully convolutional neural net- works,","venue":null,"work_id":"5cbc38cf-7539-4b55-a574-405fbdd3f988","year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.468367Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:7fdd4a45de72ff738564cdda6b34be26898cbad0954fa6f8199136a1c6d0f31d","observation_id":"da7b5738-d1ba-4f2c-8079-b216d8713ca0","resolution":{"observed_at":"2026-08-07T04:38:10.188880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.177397Z","title":"Structural and statistical texture knowledge distillation and learning for segmentation,","venue":null,"work_id":"ecb8cbb1-1b8f-4b80-a80f-2d70d365b24d","year":2025},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.471231Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a584fb0500006f00a11cb463121ba82042c0561fbe8b334a65b8d3514637507a","observation_id":"e36b5573-7951-4ffb-9ede-97d31dc40171","resolution":{"observed_at":"2026-08-07T04:38:10.180069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.169600Z","title":"A new contourlet transform with sharp frequency localization,","venue":null,"work_id":"08b9e45c-0329-4ca6-bdd7-5f0c4ebe1ae8","year":2006},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.473892Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:07c5061adcac929389ecb47f14a1a211170577d9276a3fb1d71764c347124e5d","observation_id":"e32eb825-f0c6-4db4-9bbc-0f6235f5d9aa","resolution":{"observed_at":"2026-08-07T04:38:10.172327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.161511Z","title":"The nonsubsampled con- tourlet transform: theory, design, and applications,","venue":null,"work_id":"2fe0f16e-be8a-42df-8927-50b55384b5ab","year":2006},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.476682Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e8ca3439cb58fe23bfbb97a1c4cf5775ea011361511110719dc8ead89591226d","observation_id":"40941e56-3444-4378-aee3-343a7c44d8fd","resolution":{"observed_at":"2026-08-07T04:38:10.164461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.153442Z","title":"Auto- matic multi-organ segmentation of prostate magnetic resonance images using watershed and nonsubsampled contourlet transform,","venue":null,"work_id":"74cb05f3-a485-4a9e-8307-2e73680feeb2","year":2016},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.479229Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:4eba3bddfb04579899ad55cf5853284e35e77ad83313b18973b0c4f0c7b58e6f","observation_id":"ffd6bf26-a40c-404d-8c4e-d6a57b549901","resolution":{"observed_at":"2026-08-07T04:38:10.156487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.144779Z","title":"Scheme for unsupervised colour–texture image segmentation using neutrosophic set and non- subsampled contourlet transform,","venue":null,"work_id":"87dba044-8072-4424-bb5f-1992a4cad89b","year":2016},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.482125Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:26d02db3440883519379778aabee5d74d591b9ddaf41def1b4d18257954acc76","observation_id":"0a34249e-aa45-41ab-b305-a1c54a126b00","resolution":{"observed_at":"2026-08-07T04:38:10.148121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.136299Z","title":"Brain mr image segmentation by modified active contours and contourlet transform","venue":null,"work_id":"863bc5b4-e926-487a-a8ec-26280dd41166","year":2017},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.484408Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:5d639118a926fc36a7ef4968ea8206ddf45d188e820fc08de653fa1af25277db","observation_id":"d3074fa8-e586-4cde-a96b-dddd9c33d61b","resolution":{"observed_at":"2026-08-07T04:38:10.139237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.127254Z","title":"Edge detection methods and filters used on digital image processing,","venue":null,"work_id":"6c7120e8-436f-4555-b6a8-077c0544041e","year":2017},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.487114Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e6329d238e5497271a7f7947375487d01dd758675dd27b0136a95c159a0e773b","observation_id":"2ff2f37e-429d-4301-90ca-f4af52e0bffd","resolution":{"observed_at":"2026-08-07T04:38:10.130742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.117700Z","title":"A comprehensive survey of continual learning: Theory, method and application,","venue":null,"work_id":"f5033db5-927d-476e-9f2f-dc52edba5afa","year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.489905Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:bcac7886c8d56b6d14aee3b808f47b0c54ed0b5a789a0c8faa0962618128c928","observation_id":"cecf4169-0a5a-4f17-a4ba-be2441e50087","resolution":{"observed_at":"2026-08-07T04:38:10.120825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.108597Z","title":"Prior attention network for multi-lesion segmentation in medical images,","venue":null,"work_id":"d6ed49d9-8c35-4d42-a434-fe7b7bf1ad20","year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.492628Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:95014a7373e14f9de169bdb6ced6fdd35be7e0f32b7350fafa7a7b0510d16588","observation_id":"b8319b73-5f5b-4373-b702-2372675a6e6e","resolution":{"observed_at":"2026-08-07T04:38:10.111556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.099200Z","title":"Non-separable bidimensional wavelet bases,","venue":null,"work_id":"897aa8e7-16b4-4e09-a616-51f8b90e08e1","year":1993},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.495077Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:56fcb50ff725cdc3e65754a1b17fda9db6898c0dff59bee4c41a58e75c983e71","observation_id":"1c1c56f3-e19b-4e51-9de7-1825830d8ea9","resolution":{"observed_at":"2026-08-07T04:38:10.102227Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:09.497448Z","title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.497448Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:eba48dc7d60651b7b831be75e9405a7487b892dfdc2ae9cfb437e4cab53ca228","observation_id":"eec245a3-24e6-471f-b1af-df72fd42d5df","resolution":{"observed_at":"2026-08-07T04:38:09.497448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07642","last_updated":"2023-05-12T17:52:36Z","snapshot_observed_at":"2026-07-06T15:26:34.939192Z","submitted_at":"2023-05-12T17:52:36Z","title":"The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.07642","snapshot_observed_at":"2026-08-07T04:38:09.499844Z","title":"The asnr- miccai brain tumor segmentation (brats) challenge 2023: Intracranial meningioma,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.499844Z"},"links":{"cited_paper":"/paper/2305.07642","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:06ae0fece26bbfe3ae57a9697f7966bf843c58eb532e1f9b2e8beeb1d145c667","observation_id":"9f9f32e6-8455-415d-b3da-3e1b120ceaf9","resolution":{"observed_at":"2026-08-07T04:38:09.499844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:09.503079Z","title":"The medical segmentation decathlon,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.503079Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:216f8c71f060d13b1918c5e291a529ddc2b83079a7290964c6f96bff7a236282","observation_id":"1dc5a5bf-2495-4ca0-9bd8-47d884b56561","resolution":{"observed_at":"2026-08-07T04:38:09.503079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:09.506009Z","title":"Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.506009Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a0d3439689d9d862947d09f6c6ebb6dab548039b570fe0bd01271623d75fbc83","observation_id":"476dd9f6-8c1d-4262-aa35-c4ef54c64c1a","resolution":{"observed_at":"2026-08-07T04:38:09.506009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.02629","last_updated":"2019-04-23T13:35:04Z","snapshot_observed_at":"2026-07-06T07:13:05.887491Z","submitted_at":"2018-11-05T05:10:18Z","title":"Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.02629","snapshot_observed_at":"2026-08-07T04:38:09.508741Z","title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.508741Z"},"links":{"cited_paper":"/paper/1811.02629","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:bd3bbc297fe2b77630bb1cafc3e97f37db8fa71befc036d2aae70ff852f89d27","observation_id":"d62a963b-491d-4a11-a93b-43c6cb8ff5ff","resolution":{"observed_at":"2026-08-07T04:38:09.508741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:09.511495Z","title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.511495Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:012202d60ca1192d157295bec871186757c7c729753bd9eb9dfcc155a6bd7a2d","observation_id":"4999f7ea-9360-4529-8e68-5473c93d712d","resolution":{"observed_at":"2026-08-07T04:38:09.511495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:09.513940Z","title":"Inter-slice context residual learning for 3d medical image segmentation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.513940Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:400da7aa86ec4de5ac24685b20abc51334dae13c48c8beb4dc70c85b6d674262","observation_id":"ba16d7a8-fb40-4449-a122-84a1f54cee92","resolution":{"observed_at":"2026-08-07T04:38:09.513940Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.061555Z","title":"Transbts: multimodal brain tumor segmentation using transformer, medical image computing and computer assisted intervention-miccai 2021,","venue":null,"work_id":"8fd6c181-760a-4738-b8da-ac022dae9d09","year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.516557Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:c197b3c491f8996331638915bf5d45dd3df1ae14d1e946725dcdfa940cb5da6d","observation_id":"a20097ad-3a13-4ff1-bea7-597d3a97ae00","resolution":{"observed_at":"2026-08-07T04:38:10.064982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.052803Z","title":"A robust volumetric transformer for accurate 3d tumor segmentation,","venue":null,"work_id":"0dcc1784-3de3-4dcf-9fca-9baa22150e66","year":2022},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.519519Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:a6400c6899cfaa62e4a4a85817bb59924a57de3d9274ab5ed8782d6204b9656d","observation_id":"12e785fb-9b4c-4964-b189-8bde66767e60","resolution":{"observed_at":"2026-08-07T04:38:10.055787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.043580Z","title":"Shape-scale co- awareness network for 3d brain tumor segmentation,","venue":null,"work_id":"79d43c1f-2741-46f2-9f4f-d64924f43abb","year":2024},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.522400Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:2134230d6a9b3c906f7be65fd3cfb75be1b5c1aee49f7e45a783fb0dd0d96658","observation_id":"5c579db0-3d0b-4e71-8fa8-23ce611a5884","resolution":{"observed_at":"2026-08-07T04:38:10.046904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.034895Z","title":"Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,","venue":null,"work_id":"d4fafa54-87bd-4073-93d3-69091951e974","year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.525073Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:dc27ff2ab88629c2ad35425eb7c9bfa6101e9c3fac3c7d3500cfbe5084f8dfb9","observation_id":"3cbef443-aa0f-4518-b3fb-e79e38aea52e","resolution":{"observed_at":"2026-08-07T04:38:10.037907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T04:38:10.025860Z","title":"Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,","venue":null,"work_id":"d36b1093-bea1-4f90-8af1-e4ddfde5b0e2","year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.527588Z"},"links":{"citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:e1684636af4acd268b7b582d1d4f05250c8f67036b8e8ee38ff217e251e9db40","observation_id":"f9825aad-23e8-4f6b-9356-d8596b25aaaa","resolution":{"observed_at":"2026-08-07T04:38:10.029382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2109.03201","last_updated":"2022-02-04T06:53:37Z","snapshot_observed_at":"2026-08-04T08:02:54.298886Z","submitted_at":"2021-09-07T17:08:24Z","title":"nnFormer: Interleaved Transformer for Volumetric Segmentation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.03201","snapshot_observed_at":"2026-08-07T04:38:09.530568Z","title":"nnformer: Interleaved transformer for volumetric segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T04:38:09.530568Z"},"links":{"cited_paper":"/paper/2109.03201","citing_paper":"/paper/2506.10142"},"observation_digest":"sha256:910792f96637761e0089d40195a3da10bc9ecaf97995254295697c664f0d4d53","observation_id":"1d1158a5-e66c-4fbf-8b57-ffbb8cb06ae8","resolution":{"observed_at":"2026-08-07T04:38:09.530568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.10142","last_updated":"2025-06-11T19:44:51Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-07T04:30:54.240681Z","submitted_at":"2025-06-11T19:44:51Z","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":65,"verified_exact":2,"verified_fuzzy":33},"total_outbound_references":127},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 127 outbound references and 0 inbound Pith citation observations for arXiv:2506.10142."}