{"as_of":"2026-08-08T23:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:36dac1189be68e964990f95a96a5141e5169fecee91cc74cf794d1c00e5b809e","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:41:56.924092Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"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/2507.17971/citation-record","integrity":"/paper/2507.17971/integrity","json":"/paper/2507.17971/citation-record.json","paper":"/paper/2507.17971"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.06463","last_updated":"2024-11-14T19:06:05Z","snapshot_observed_at":"2026-07-06T18:12:34.664133Z","submitted_at":"2024-05-10T13:15:42Z","title":"MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06463","snapshot_observed_at":"2026-08-06T14:41:56.738534Z","title":"MRSegm en- tator: Robust multi-modality segmentation of 40 classes in MRI and CT sequences,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.738534Z"},"links":{"cited_paper":"/paper/2405.06463","citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:6aca5d7f4dfba005549756d9711ce80f12b0f58ff5e9086864b919f69f609366","observation_id":"a18afcc0-1c60-4db6-81b8-c2367bffe17a","resolution":{"observed_at":"2026-08-06T14:41:56.738534Z","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":"2026-08-06T14:41:57.757644Z","title":"UK bi obank: an open access resource for identifying the causes of a wide r ange of complex diseases of middle and old age,","venue":null,"work_id":"e14d9492-d783-43cd-97e6-954339531c1c","year":2015},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.742530Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:171959f46039d3fa21dc0e33ebc1663df85ea7bb55cf7a59ea0f73d0cc02e6a5","observation_id":"c312cd7e-f0d4-47ce-ad25-69cd1c4fdad4","resolution":{"observed_at":"2026-08-06T14:41:57.762066Z","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":"2026-08-06T14:41:57.746976Z","title":"TotalSegme ntator: Robust segmentation of 104 anatomic structures in CT images ,","venue":null,"work_id":"13f19743-3188-48c4-914a-40a93679f51a","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.745661Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:328c23c2c2ae01eea89b86189670ea8899c8f99521aca32f3feeb81299a86d65","observation_id":"e84c59b6-e4c3-45bb-bf99-af1e46d8b92a","resolution":{"observed_at":"2026-08-06T14:41:57.750676Z","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":"records/6802614","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:41:57.190488Z","title":"Dataset with segmentations of 104 importan t anatomical structures in 1204 CT images,","venue":null,"work_id":"e3a56ef3-8183-4556-a872-1d82bd768565","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.750158Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:27b829742ce7749511edc0926aa7eaf18ca0df8cfa57947e3c8ad9a186371f34","observation_id":"388941b5-459a-4947-8d90-62c3def003d1","resolution":{"observed_at":"2026-08-06T14:41:57.196555Z","resolver_source":"raw_fallback","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":"2405.05944","last_updated":"2024-06-24T18:05:06Z","snapshot_observed_at":"2026-08-01T23:46:14.241144Z","submitted_at":"2024-05-09T17:33:09Z","title":"MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05944","snapshot_observed_at":"2026-08-06T14:41:56.754061Z","title":"MRISegmentator-Abdomen: A full y automated multi-organ and structure segmentation tool for T1-weighted abdominal MRI,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.754061Z"},"links":{"cited_paper":"/paper/2405.05944","citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:a4316aabde166ad5fa2ebbe05c56238869cef9dd253c235f3e614b51a8cc9911","observation_id":"fd4f2a41-20cc-422d-aef1-df0661a79008","resolution":{"observed_at":"2026-08-06T14:41:56.754061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19492","last_updated":"2025-02-26T12:27:21Z","snapshot_observed_at":"2026-07-06T18:22:18.644161Z","submitted_at":"2024-05-29T20:15:54Z","title":"TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19492","snapshot_observed_at":"2026-08-06T14:41:56.758494Z","title":"TotalSegmentator MRI: Sequence-independent segmen tation of 59 anatomical structures in MR images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.758494Z"},"links":{"cited_paper":"/paper/2405.19492","citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:774b41daa2a7e1569b19c54ce109efe8aa67bb9b75933adba3b674faf04c2726","observation_id":"24c81180-31c9-410a-816b-38728e031329","resolution":{"observed_at":"2026-08-06T14:41:56.758494Z","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":"2026-08-06T14:41:57.736072Z","title":"N a- tional cancer institute imaging data commons: Toward trans parency, reproducibility, and scalability in imaging artificial int elligence,","venue":null,"work_id":"2c08dfb6-c99b-4f6d-b31b-cda177d03310","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.763572Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:88ff8defa2ef06fdb3bf182f1979b0ed8c7578545690f2f71a6e144c52d16a5c","observation_id":"6de83806-807a-4d11-841a-ccb76f6cb236","resolution":{"observed_at":"2026-08-06T14:41:57.739884Z","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":"2026-08-06T14:41:57.723151Z","title":"Auto mated segmentation of tissues using CT and MRI: a systematic revie w,","venue":null,"work_id":"674522e5-71de-4da4-b020-67f1bb958439","year":2019},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.767197Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:95b9510a2971df43547df4386a4f21fae6b341827b3ef4ecac9a75ab49f7f466","observation_id":"68154771-2c8a-4b76-b9c5-7c6b27367645","resolution":{"observed_at":"2026-08-06T14:41:57.727448Z","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":"2026-08-06T14:41:57.711216Z","title":"Pancreas volume in health and disease: a systematic review and meta- analysis,","venue":null,"work_id":"61879f27-0013-455b-a599-06bdd3093c8a","year":2018},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.770663Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:44e72411587ff765ff16b818e8f17186e0421b435d52cf0d5e3bfdd0db84fab1","observation_id":"dd88fa4a-379e-44ad-a0a8-500407cd9d8a","resolution":{"observed_at":"2026-08-06T14:41:57.715458Z","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":"2026-08-06T14:41:57.698485Z","title":"Assessment of kidney volumes from mri: acquisit ion and segmentation techniques,","venue":null,"work_id":"2d6e8383-161e-46e1-928e-03a76b0f076a","year":2012},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.774657Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:3bcdec2872e444a959d3884794895a2f7172443d80751a603830b8e904d07888","observation_id":"1dc28dae-f4d3-4914-bf7c-e196d6a912cc","resolution":{"observed_at":"2026-08-06T14:41:57.702677Z","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":"2026-08-06T14:41:57.687871Z","title":"The value of m agnetic resonance imaging for radiotherapy planning,","venue":null,"work_id":"a9f5589a-188c-4086-abb4-9f5e5556f17b","year":2014},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.778559Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:ca94faa1cbecb80b96a027a72552515e6a43c568e276d6420b53209056e3ee89","observation_id":"0bd60846-f8aa-47e5-b42e-0048c92ee10a","resolution":{"observed_at":"2026-08-06T14:41:57.691286Z","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":"2026-08-06T14:41:57.676748Z","title":"Integrated MRI-guided radiotherapy—opportunities and c hallenges,","venue":null,"work_id":"caae5003-cd2c-44db-b7b4-5350ff64b5c5","year":2022},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.782053Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:bf0a2bef39cfc113e41bbd095dd9a61e9ccec24b4f775e73fed0017a31de61ee","observation_id":"1c67a96f-f76d-427b-b7b7-f697e088a746","resolution":{"observed_at":"2026-08-06T14:41:57.679822Z","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":"2026-08-06T14:41:57.666146Z","title":"Radiomics: images ar e more than pictures, they are data,","venue":null,"work_id":"03549a6b-4d26-4882-bfed-7751b6f526c9","year":2016},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.785423Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:f47263375ac7c07448e6b6e7c2fee485d47ea3de203bd2b22e5da319e45aad4c","observation_id":"b6538e5a-a8ab-4a6e-92e9-3f1ab5eec6ff","resolution":{"observed_at":"2026-08-06T14:41:57.670296Z","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":"2026-08-06T14:41:57.655151Z","title":"CT and MRI of abdominal can cers: current trends and perspectives in the era of radiomics and a rtificial 10 IEEE TRANSACTIONS ON MEDICAL IMAGING intelligence,","venue":null,"work_id":"abef0971-a703-420e-ba8d-b29e890698b2","year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.788742Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:ec23589bfeb263b1e774c80b4ad08d4bd6a8f71660f86fd378de68753b3d0d78","observation_id":"cdd01f44-3b3e-4f36-8c65-c6975e541fd7","resolution":{"observed_at":"2026-08-06T14:41:57.658605Z","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":"2026-08-06T14:41:57.644196Z","title":"Recommendations for MRI-based contouring of gross tumor v olume and organs at risk for radiation therapy of pancreatic cance r,","venue":null,"work_id":"5e9f1af7-af5d-4a6f-8095-d0de984d4f54","year":2017},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.791742Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:bc7278b48697be81c002ad4bcb356e9dacd3efc29166f1066ec7fa7af61f9569","observation_id":"92a1b546-bded-4f91-8d75-19aa1455e926","resolution":{"observed_at":"2026-08-06T14:41:57.648360Z","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":"2026-08-06T14:41:57.633555Z","title":"vOARiability: Interobserver and intermodality variabil ity analysis in oar contouring from head and neck CT and MR images,","venue":null,"work_id":"3e346772-982c-404a-bae3-17c6710f7a27","year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.795405Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:fd511eb0926cdf0075e305ab86056c81df0742257bc6dfa15fa66702ffc106dd","observation_id":"b403d3c8-c59c-4290-81ab-9437cb32e12a","resolution":{"observed_at":"2026-08-06T14:41:57.637207Z","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":"2026-08-06T14:41:57.622547Z","title":"U-Net: Convolutio nal net- works for biomedical image segmentation,","venue":null,"work_id":"767d0654-b209-41a1-895d-291c9ce3550c","year":2015},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.799247Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:0c31fce3ae439c865d1e06a7121ad4ad053c04e4ab80d8983ec699b43d8de297","observation_id":"3b2b1e46-7423-450a-8f98-4c268e510db6","resolution":{"observed_at":"2026-08-06T14:41:57.625953Z","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":"2026-08-06T14:41:57.611754Z","title":"A learning strategy for contrast-agnostic mri se gmentation,","venue":null,"work_id":"5d647a25-be86-463a-92aa-6dda033b9642","year":2020},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.802179Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:044fe5d9054b8b1d33326559d2a4f6ee51947b97ea7670a8aed541cbed1a8c76","observation_id":"470f7fa7-fc6b-4a34-8918-0f7de11e7b5e","resolution":{"observed_at":"2026-08-06T14:41:57.615321Z","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":"2026-08-06T14:41:57.599865Z","title":"A survey on transfer learning,","venue":null,"work_id":"b4e93a12-8de7-4588-b98a-c1bab33f402b","year":2010},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.805986Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:f1b5bf22402430cb8aeeef9f1a7bc04aad5b7167e0843f6f26c49bdfd4d600a1","observation_id":"4f5cb893-91c1-4788-be1d-8f98a352ab61","resolution":{"observed_at":"2026-08-06T14:41:57.603570Z","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":"2026-08-06T14:41:57.588722Z","title":"Fully automated multiorgan segmentation in abdom- inal magnetic resonance imaging with deep neural networks,","venue":null,"work_id":"34607730-b0c8-4757-b1e7-145896d7757a","year":2020},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.809704Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:fc0c1e8a808ef5ea6dbab31ccb08eb869f8fa1f1175660a2676aa175dba8dba9","observation_id":"b1fd6356-bb9f-44cb-8d00-913fe61a85b9","resolution":{"observed_at":"2026-08-06T14:41:57.592887Z","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":"2026-08-06T14:41:57.577994Z","title":"Deep learning-bas ed auto- mated abdominal organ segmentation in the uk biobank and ger man national cohort magnetic resonance imaging studies,","venue":null,"work_id":"899a9d64-026d-4671-a173-996c95967047","year":2021},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.813172Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:865445cc5be71a01c3b698acc9ae74cab0c1d41c90582c97cce4696dcd4896ef","observation_id":"399dbaef-e2ad-402b-9320-fbee85fc0a60","resolution":{"observed_at":"2026-08-06T14:41:57.581771Z","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":"2026-08-06T14:41:57.563345Z","title":"Abdomennet: deep neural network for abdomin al organ segmentation in epidemiologic imaging studies,","venue":null,"work_id":"f0262607-857d-4ee7-a4ff-2dbb6e23f649","year":2022},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.817639Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:23f8b2fd63b8009ffe8e2e47c9b2a3f821f4c1e7e8d3262884a1721b389fe3f3","observation_id":"5843db3f-fd36-4bd1-96d5-417f210a5229","resolution":{"observed_at":"2026-08-06T14:41:57.568009Z","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":"2026-08-06T14:41:57.551932Z","title":"Deep learning auto-segmentation on multi- sequence magnetic resonance images for upper abdominal org ans,","venue":null,"work_id":"8a6f553d-e540-4d23-9804-e5e44b9ba9c7","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.821037Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:ade720abb2465f8a97b93e05cbaba7e16b3f439b3d87f0fec6cda50ce25b34c2","observation_id":"e717c616-52d7-478a-876e-79c4c9baba95","resolution":{"observed_at":"2026-08-06T14:41:57.556754Z","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":"2026-08-06T14:41:57.541319Z","title":"nnU-Net: a self-configuring method for deep learning-based biomedic al image segmentation,","venue":null,"work_id":"00b7df5a-8bef-4515-8fee-85c7e591e8c6","year":2021},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.825095Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:e53389b65cc505e2d6c6b4c070d3b57a3cd479080d7419059e62772a8ae19548","observation_id":"646d0d39-33ae-4346-9fec-b6cc28a2397c","resolution":{"observed_at":"2026-08-06T14:41:57.544910Z","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":"2026-08-06T14:41:57.529669Z","title":"The german national cohort: aims, st udy design and organization,","venue":null,"work_id":"fa5b79bc-48a8-4b2e-b573-bc2f875c1306","year":2014},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.828535Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:de036138071a747c98113e7e1dce00af8b6f7af15e70012fb8b0db33274a2635","observation_id":"753a3ac5-c3ca-4714-824a-9d051ff44064","resolution":{"observed_at":"2026-08-06T14:41:57.533862Z","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":"2026-08-06T14:41:57.517632Z","title":"Lymph node detection in t2 mri with transformers,","venue":null,"work_id":"bcdeb6b2-640f-488d-8365-bf97ef4badc5","year":2022},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.832093Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:d1b47ec55a93134fd58ae1a629062c9043a62497394d1b38ae7b2c3167ad965a","observation_id":"b3342518-97c3-4742-8e4d-6d70f5d10342","resolution":{"observed_at":"2026-08-06T14:41:57.521716Z","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":"2026-08-06T14:41:57.504477Z","title":"Universal l ymph node detection in t2 mri using neural networks,","venue":null,"work_id":"cd5caa6c-8e51-40ab-92ee-84a2712401fe","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.835495Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:8e58756ca76e2621fa36b7274e1744753b09f2d5052040ad3c29ba280bb7be27","observation_id":"57aa8d1f-2bb5-4414-8d93-784835ef9d9b","resolution":{"observed_at":"2026-08-06T14:41:57.508227Z","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":"2026-08-06T14:41:57.493156Z","title":"Domain shift in computer vision models for M RI data analysis: an overview,","venue":null,"work_id":"590b7a45-8796-4328-af93-4b50e4d0d4c2","year":2021},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.839130Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:d468fc67b833b24dc82a593e81d0fd3de56094bf571a598affdfc582b3e3ae32","observation_id":"2fc1b51e-b23c-42d5-937e-ee9875aa9e69","resolution":{"observed_at":"2026-08-06T14:41:57.496677Z","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":"2026-08-06T14:41:57.481800Z","title":"A 3d unsupervised domain adaptation framework combining style translation a nd self- training for abdominal organs segmentation,","venue":null,"work_id":"9c7ad4c5-adbf-4ef1-8f48-d36f8b2aece6","year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.842447Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:d9efca3cbd409f6375a5a4c368a9485260f0dd0d869db8d3e256b5f7969a0ea1","observation_id":"d80889fc-67f2-4379-9fe6-7bb9a9799a19","resolution":{"observed_at":"2026-08-06T14:41:57.485434Z","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":"2026-08-06T14:41:57.470759Z","title":"Unsupervi sed domain adaptation for abdominal organ segmentation using p seudo labels and organ attention cyclegan,","venue":null,"work_id":"27419bf2-92cf-4476-8088-05ab93996393","year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.845490Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:00e7b18695a475ff285f6a66a100dae16cf81378d666038bdcf93fba8e65a892","observation_id":"cb1fe4cb-cec3-4195-b331-8f3d7166371b","resolution":{"observed_at":"2026-08-06T14:41:57.474436Z","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":"2026-08-06T14:41:57.459577Z","title":"Unpaired image -to- image translation using cycle-consistent adversarial net works,","venue":null,"work_id":"65f219b5-b4f9-4418-81de-a8db6d4c889d","year":2017},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.848480Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:cd1613b6975b1fc96a3af1be6d74ca7ed2a73d74d660b4adb65b5374757aa5ee","observation_id":"e74d26a8-40a7-4c86-b6a1-35b6ac7339cc","resolution":{"observed_at":"2026-08-06T14:41:57.463470Z","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":"2026-08-06T14:41:57.447582Z","title":"SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining,","venue":null,"work_id":"28f3497a-116c-442f-b99b-cc51774d7216","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.851648Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:c868a4afd2c78eba8c0b5ac5fa9c7ce27ddb54542639c3ae06917f905b6976c3","observation_id":"22a5968f-2738-4e25-93fa-4f6b5c4104c3","resolution":{"observed_at":"2026-08-06T14:41:57.451928Z","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":"2026-08-06T14:41:57.436559Z","title":"Robust machine learning segmentation for large-scale ana lysis of heterogeneous clinical brain mri datasets,","venue":null,"work_id":"eb1d5c6a-178d-4132-beee-bbd82d4c7baa","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.855667Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:c8064344698ce2ef4b2760dab90c17fe4ea43d6c8c349dbdba780bbc3f4bafaa","observation_id":"8dac8db5-f886-4d10-a20e-6521309d9d2e","resolution":{"observed_at":"2026-08-06T14:41:57.440620Z","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":"2026-08-06T14:41:57.425013Z","title":"Domain randomization for transferring deep neural networ ks from simulation to the real world,","venue":null,"work_id":"135ce1c4-31ab-4fb3-8322-af394c259cff","year":2017},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.859056Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:26b0076a1ccae48900134898baec775a26e33baf8d25ea8df9f11bf8b2504c9f","observation_id":"ae14da53-d0c9-4ff3-9250-44fcc1eb8151","resolution":{"observed_at":"2026-08-06T14:41:57.429181Z","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":"2026-08-06T14:41:57.411731Z","title":"AMOS: A large-scale abdominal mult i- organ benchmark for versatile medical image segmentation,","venue":null,"work_id":"caef197d-76d8-49b0-b7aa-8d1bd362683f","year":2022},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.861968Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:d710adcf678450797f3f0c3709799c1769baa90a5cae9dc84680b218b4ec5c28","observation_id":"ea7d5fb6-819a-4138-8a6d-28a9858a9100","resolution":{"observed_at":"2026-08-06T14:41:57.416020Z","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":"records/3431873","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:41:57.076889Z","title":"CHAOS - combined (CT-MR) healthy abdominal organ segmentation cha llenge data,","venue":null,"work_id":"bcccbe08-0c4c-4230-80c3-4ab34779e4cf","year":2019},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.865897Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:9e07833641ec2ea0b4a3a26666b75ae75a53a2545f839ce60d2d498b92ef65d9","observation_id":"1c13bdae-1e0f-4b24-9083-8724b321faaf","resolution":{"observed_at":"2026-08-06T14:41:57.085042Z","resolver_source":"raw_fallback","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:41:57.399580Z","title":"Comparison of semi- automatic and deep learning-based automatic methods for li ver segmen- tation in living liver transplant donors,","venue":null,"work_id":"06a1efa1-d782-470f-ad3d-884da864bbbf","year":2020},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.870145Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:0353811def128190f66f245334085fe60e20ec01469f613878ecbb8c40264a9b","observation_id":"787f4de0-9232-4bed-9547-590e10829079","resolution":{"observed_at":"2026-08-06T14:41:57.403291Z","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":"2026-08-06T14:41:57.386106Z","title":"CHAOS challenge- combined (CT-MR) healthy abdominal organ segmentation,","venue":null,"work_id":"d8f15ed8-7404-4ef7-925b-8a4056bd86ac","year":2021},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.874032Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:f144157a44e3d3952f2fa1b26589b204cdfbb3c90e67cea27e45f4ee44a3e7a0","observation_id":"b7530042-2a43-4647-b7ef-102248310bb2","resolution":{"observed_at":"2026-08-06T14:41:57.391003Z","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":"2026-08-06T14:41:57.372167Z","title":"Liver- HCCSeg: A publicly available multiphasic MRI dataset with l iver and HCC tumor segmentations and inter-rater agreement analysi s,","venue":null,"work_id":"51b6b26d-9479-4023-acd4-5a5c1ace221a","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.878012Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:47abb91131e8f9208da6e04f270d41405bdc8bdff09125cd103d745a0c85af4e","observation_id":"72e0c473-22de-4ed8-b0dd-387a13babbf5","resolution":{"observed_at":"2026-08-06T14:41:57.377050Z","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":"2026-08-06T14:41:57.361207Z","title":"L iverHC- Ceg: A publicly available multiphasic MRI dataset with live r and HCCC tumor segmentations and inter-rater agreement analysis,","venue":null,"work_id":"47496ab6-7145-4c68-bdae-134b15c06df2","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.881121Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:b02ced7aae509f682b93eb561ba7831fc73af52d7ed5f69721d6ad13c9f4264e","observation_id":"304059ef-7002-4679-929b-c8f0862635e4","resolution":{"observed_at":"2026-08-06T14:41:57.365099Z","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":"2026-08-06T14:41:57.349724Z","title":"Monai label: A fra mework for ai-assisted interactive labeling of 3D medical images,","venue":null,"work_id":"c1e392dc-bbdb-4542-869f-cbf6d13b46b6","year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.884200Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:fd2581204bdf1a1e891bed9cd197253ec6a8caf4327bbed7ae9a11be21dd973b","observation_id":"6a193a5b-a8d6-437f-8471-a45915d0ec4a","resolution":{"observed_at":"2026-08-06T14:41:57.354368Z","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":"2026-08-06T14:41:57.339711Z","title":"Segment ation of pelvic structures in T2 MRI via MR-to-CT synthesis,","venue":null,"work_id":"51bbd15c-a490-40b1-af01-a768fdb6bcd7","year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.887333Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:cd0fa061f8fd681cf3f0f7008e48c96c85998d85d3a6e45a3961ccaaf4ee3694","observation_id":"f7ea22c9-8a7c-4a88-b957-6e764142c849","resolution":{"observed_at":"2026-08-06T14:41:57.343158Z","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":"2026-08-06T14:41:57.329085Z","title":"Fully convolutional neural netw orks for volumetric medical image segmentation,","venue":null,"work_id":"8d41db82-1eab-4bdc-b461-54985c884c5e","year":2016},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.890299Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:f57efab400d509cb4ca858061d5f75d1da13c110953b5dad5836e783c0c13057","observation_id":"b247789d-3259-4080-a0ff-a4b0acb4bb2b","resolution":{"observed_at":"2026-08-06T14:41:57.332828Z","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":"2026-08-06T14:41:57.318432Z","title":"Jetstream2: Accelerating cloud computing via j etstream","venue":null,"work_id":"3b324996-70bb-40cb-892a-b89813c2980d","year":2021},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.893707Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:f4ecda8924b46373163b371be3b9027b488e7562695bdb88b7afc1dd6e490e40","observation_id":"8b55eb88-6134-4a90-a63e-937e45ff3e4f","resolution":{"observed_at":"2026-08-06T14:41:57.321987Z","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":"2026-08-06T14:41:57.306005Z","title":"ACC ESS: Advancing innovation: NSF’s advanced cyberinfrastructur e coordination ecosystem: Services & support,","venue":null,"work_id":"ba92aa06-5586-4ea0-86ef-12062617dcf8","year":2023},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.896676Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:d66983f7578932d5ec55eaf2a5b57d5791d45da097c5bfa1e408f2a8457a14ac","observation_id":"48693700-1b30-4b4b-8bf0-05d2a460e037","resolution":{"observed_at":"2026-08-06T14:41:57.309808Z","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":"2026-08-06T14:41:57.293369Z","title":"The cancer genome atlas liver hepatocellular carcinoma collec tion (TCGA-LIHC) (version 5) [data set],","venue":null,"work_id":"794106de-18f5-4425-92af-6aceddd083b0","year":2016},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.899510Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:ded903647ff1479a067509b604856d7a3a9f7b4219bb9fc5ea74d5ade220f91b","observation_id":"4919b2cb-7dea-495f-8d43-ec43e7095dcb","resolution":{"observed_at":"2026-08-06T14:41:57.297078Z","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":"2026-08-06T14:41:57.281105Z","title":"Measures of the amount of ecologic association bet ween species,","venue":null,"work_id":"fef8100b-8fa9-4ac5-88e3-309872f9ee66","year":1945},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.903159Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:5d161995106d08dd95abcb3657d7aedcc86cb9983f8a9652c2b736dc773a0831","observation_id":"366e5308-6804-48ba-b696-97260763eeb0","resolution":{"observed_at":"2026-08-06T14:41:57.285080Z","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":"2026-08-06T14:41:57.266766Z","title":"Compari ng images using the hausdorff distance,","venue":null,"work_id":"4a276a3a-5553-45c7-8f66-fa0433afde4d","year":1993},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.906844Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:7db81f9994b739a459744ad6a35d3f9b72fdf686c6c67d2885fa20a95218eec4","observation_id":"778f7af3-b9ff-486a-b52c-70cfa806fbeb","resolution":{"observed_at":"2026-08-06T14:41:57.271848Z","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":"2026-08-06T14:41:57.254131Z","title":"Segmentatio n precision of abdominal anatomy for mri-based radiotherapy ,","venue":null,"work_id":"1fb01366-2be2-426b-b02b-80221133967d","year":2014},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.909746Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:f9474f51eb1058fea03578dea8fdfef1addf6425da8f2caf9528b74152832feb","observation_id":"4fd21efd-9fd7-43ac-a9f0-048a0150f6d3","resolution":{"observed_at":"2026-08-06T14:41:57.258212Z","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":"2026-08-06T14:41:57.242281Z","title":"Pancreas segmenta tion in mri using graph-based decision fusion on convolutional n eural net- works,","venue":null,"work_id":"32219ae4-d5bd-413a-97eb-69cdc085f905","year":2017},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.912751Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:b082d5f1f62e75100cb9bae73004a2f18874e3d93e30a223490da1c9074e4a41","observation_id":"4bd27145-205d-4a0c-9ea7-52f373cd7da0","resolution":{"observed_at":"2026-08-06T14:41:57.246115Z","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":"2026-08-06T14:41:57.231066Z","title":"Noise and the reality gap: The use of simulation in evolutionary robotics,","venue":null,"work_id":"e16bae33-98a8-4e04-9e67-3cabb1c9aaca","year":1995},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.916278Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:728ebee29f4476dfc6cd3f4793a2330c74b1b67d1d80f4770ae8e88627f84e93","observation_id":"d9b1c9ae-db4e-4fdd-9a84-67756adf773a","resolution":{"observed_at":"2026-08-06T14:41:57.234694Z","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":"2406.00125","last_updated":"2025-09-08T14:44:54Z","snapshot_observed_at":"2026-08-01T03:52:18.046940Z","submitted_at":"2024-05-31T18:32:46Z","title":"VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00125","snapshot_observed_at":"2026-08-06T14:41:56.919932Z","title":"To- talvibesegmentator: Full body mri segmentation for the nak o and uk biobank,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.919932Z"},"links":{"cited_paper":"/paper/2406.00125","citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:f51fdc775a036d3c863e404e3d2bad2ae4d6bc0a92e0f7672fa9ffd3daff3bf7","observation_id":"43318a0f-1e9f-4efd-baad-81bea275e0ce","resolution":{"observed_at":"2026-08-06T14:41:56.919932Z","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":"2026-08-06T14:41:57.217066Z","title":"Maximum likelihood fro m incomplete data via the EM algorithm,","venue":null,"work_id":"0edb5079-757b-4cd6-a20e-0871f68d6787","year":1977},"citing_paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T14:41:56.924092Z"},"links":{"citing_paper":"/paper/2507.17971"},"observation_digest":"sha256:bedaf0281ef1565040e42ff4853faa7a8821984ae44348ac3f749e28fe63aba4","observation_id":"fc331ae1-7cf5-40d2-b142-c3bb847c307e","resolution":{"observed_at":"2026-08-06T14:41:57.221458Z","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"}}],"paper":{"arxiv_id":"2507.17971","last_updated":"2025-07-25T14:03:46Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-06T14:36:20.064751Z","submitted_at":"2025-07-23T22:37:26Z","title":"Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":2,"verified_fuzzy":47},"total_outbound_references":53},"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 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2507.17971."}