{"as_of":"2026-08-14T15:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e9e20b712ed2b8d70c25fcc26e501c26f0c4325c786538d1d4e7889d44ecdcb8","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T03:25:44.104161Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2605.23183/citation-record","integrity":"/paper/2605.23183/integrity","json":"/paper/2605.23183/citation-record.json","paper":"/paper/2605.23183"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and ra- diomic features.Scientific data, 4(1):1–13","venue":null,"work_id":"e621e566-e5a7-4583-b8c2-08068cbcabc9","year":2017},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:037040a8dacfd8798bdcb8d16b30624926e272ddd58e8efa741f213094a04580","observation_id":"3ba5f642-f7e2-416f-98eb-5f33d349702b","resolution":{"observed_at":"2026-05-25T03:26:36.325381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Mul- timodal disentangled variational autoencoder with game theoretic interpretability for glioma grading.IEEE jour- nal of biomedical and health informatics, 26(2):673–684","venue":null,"work_id":"80303340-a2df-4396-9d88-2cceca9155e1","year":2021},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:39531a2cd1c161d4557fa23319c9636b444c659d98c713bcc1893f2d21565e15","observation_id":"36d0c99a-2881-4bdb-a555-c6fb2fd0eb0b","resolution":{"observed_at":"2026-05-25T03:26:36.328727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"A fully automated multimodal mri- based multi-task learning for glioma segmentation and idh genotyping.IEEE Transactions on Medical Imaging, 41(6):1520–1532","venue":null,"work_id":"c5cffede-46c3-4da8-a7de-841684b335e5","year":2022},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:ea8ad13d1c276cfc085447606469fb09d87605509d8e3c07f022f9ceabee06bc","observation_id":"218d5034-e598-4274-ab27-8f20b3b4ef34","resolution":{"observed_at":"2026-05-25T03:26:36.276947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Fully automated hybrid approach to pre- dict the idh mutation status of gliomas via deep learning and radiomics.Neuro-oncology, 23(2):304–313","venue":null,"work_id":"82d98da1-a5d8-4541-8f0c-ad7e40647122","year":2021},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:597f1c107ccfa65642c84433b057c7ba0c9298fee0400d0b81f198c83a515e16","observation_id":"4e9eecd0-9529-47ed-b683-9207f02f1b7d","resolution":{"observed_at":"2026-05-25T03:26:36.280437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Decou- pled kullback-leibler divergence loss.Advances in Neural Information Processing Systems, 37:74461–74486","venue":null,"work_id":"36395b14-7906-47e8-9769-3d21741b0edd","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:7e0b5c203741597595b3f176c86161876786b91bf2e4f3d93be1ddc2c9db0f3c","observation_id":"203b565a-6c74-4d0b-89aa-13795a92b3ee","resolution":{"observed_at":"2026-05-25T03:26:36.304958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Vision transformer-based glioma classification using multi-modal mri and wavelet fusion","venue":null,"work_id":"bc464dec-9213-4efc-9114-acf29832291b","year":2025},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:a88c88832102b6a913d0e9409add4b06e13bbbeaf2d1b907ddaec2331300e39d","observation_id":"0b61b48e-bc55-4a50-a5d4-0e2a59f87443","resolution":{"observed_at":"2026-05-25T03:26:36.283567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Glioma groups based on 1p/19q, idh, and tert promoter muta- tions in tumors.New England Journal of Medicine, 372(26):2499–2508","venue":null,"work_id":"c7382ae7-8df7-4acb-942d-46d8c4299997","year":2015},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:605f0a13cff31f6e182f804a75e4f385ae141d847d55f364f22c8e15574a0ae5","observation_id":"7d1f8dde-fbbe-4ae7-928b-e7e7ec8d4b53","resolution":{"observed_at":"2026-05-25T03:26:36.311189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Masked au- toencoders are scalable vision learners","venue":null,"work_id":"94118db7-2630-4288-85fb-5f1a81141280","year":2022},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:26d02bbf052036e1ae8f78593d4597e88f7f6715c3fbce4a4ede08aee1547598","observation_id":"09848a5f-d198-4a0d-b252-12fcd71ffe5d","resolution":{"observed_at":"2026-05-25T03:26:36.301740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Uda-gs: A cross- center multimodal unsupervised domain adaptation frame- work for glioma segmentation.Computers in Biology and Medicine, 185:109472","venue":null,"work_id":"1af9b3ec-71e3-4fd2-a8c9-8e3f2030150b","year":2025},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:7b15e9ea846e70416be7527e7e80bf055ecf881255bfe2f0c8199049ecc3c201","observation_id":"ec913045-3988-45b4-ba4f-a5342793bdf0","resolution":{"observed_at":"2026-05-25T03:26:36.314533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Semi-supervised learning for medical image classification using imbalanced training data.Computer methods and programs in biomedicine, 216:106628","venue":null,"work_id":"bbf1d152-ab07-47a7-968f-ee042e59b68d","year":2022},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:f31ee96ab45c70cb45a4b831807519e1028119318532a9d8c7eff897be0b12c5","observation_id":"d3d78fea-0954-45fd-a301-68446d6e3eec","resolution":{"observed_at":"2026-05-25T03:26:36.317962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Unsupervised contour tracking of live cells by mechanical and cycle consistency losses","venue":null,"work_id":"fc2f4d77-e580-4593-969f-0a9194795693","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:b32f179eab2efd090e17aeb182ea7989ca7252bbec672b2a77dfea25465ab226","observation_id":"be61721c-28ba-45f7-b6f7-cb60157c4ddd","resolution":{"observed_at":"2026-05-25T03:26:36.257368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Perceptual losses for real-time style transfer and super-resolution","venue":null,"work_id":"cc9cea06-002b-410b-ad82-9be1097fca8f","year":2016},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:550f4905ed463455739b3d55e06d6c4709737d00cc8b5798418f78d7ef200fdd","observation_id":"7536e88b-ce58-4e57-80c8-d4633fc4bbd8","resolution":{"observed_at":"2026-05-25T03:26:36.308058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Gcnet: Graph completion net- work for incomplete multimodal learning in conversation","venue":null,"work_id":"f2bf98aa-3c1b-4f79-b21a-d6b4e93d1a44","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:a085e5ad2941f664d757c89356df8c84b049313bee05e729c37e85664ae6316e","observation_id":"772bde15-aa26-4286-9b28-1a2636ea6d15","resolution":{"observed_at":"2026-05-25T03:26:36.270479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17162","last_updated":"2023-11-28T19:00:29Z","snapshot_observed_at":"2026-08-13T05:15:04.355630Z","submitted_at":"2023-11-28T19:00:29Z","title":"Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder","version":1},"cited_work":{"arxiv_id":"2311.17162","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2311.17162","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fast particle-based anomaly detection algorithm with varia- tional autoencoder.arXiv preprint arXiv:2311.17162","venue":null,"work_id":"d17da966-0508-47d8-95a1-16b3222faa23","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"cited_paper":"/paper/2311.17162","citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:d5c89c76ca26053ed78f2c60e99250e4b63c791d20ab5637b482090c287b8e58","observation_id":"08813550-bf70-4115-aa90-e4a317a6101b","resolution":{"observed_at":"2026-05-25T03:26:35.628895Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"The 2021 who classification of tumors of the central nervous system: a summary.Neuro- oncology, 23(8):1231–1251","venue":null,"work_id":"5a08fba9-3ca9-4889-8365-676874f8ce37","year":2021},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:09daf563969e18f525415fc5ec0a07a3aed17131568b37aa54fb599e2c1d0aec","observation_id":"9b189202-8f04-49fe-acc9-addb60b4eb1f","resolution":{"observed_at":"2026-05-25T03:26:36.263510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Multi-modal modality- masked diffusion network for brain mri synthesis with ran- dom modality missing.IEEE Transactions on Medical Imaging, 43(7):2587–2598","venue":null,"work_id":"19a32c44-4998-4f09-b309-f43776695f47","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:22c6e81be4d0f12279293101a1243f5be746c4850fa0386e1fd92b551dacfcf1","observation_id":"86e5fcab-2436-4404-9f4b-d923d6a3ad62","resolution":{"observed_at":"2026-05-25T03:26:36.273823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"A review of the economic burden of glioblastoma and the cost effectiveness of pharmacologic treatments.Pharmacoeconomics, 32:1201–1212","venue":null,"work_id":"45ac13ee-d036-40f1-be04-2ce0120ab96a","year":2014},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:320986f596a2cf34863779af5da600a686cebd352cbc29d27ba9423ca7141725","observation_id":"9631956e-305f-451e-a134-469019ac7a10","resolution":{"observed_at":"2026-05-25T03:26:36.284269Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Idh1 mutations as molecular signature and predictive factor of secondary glioblastomas.Clinical Cancer Research, 15(19):6002–6007","venue":null,"work_id":"1e263185-4a31-484a-a997-783e1219a75c","year":2009},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:28fc84886a93c0a023aaa9842724692dcc7b2a1fc85397191ce75e4365d0e4fb","observation_id":"049a20da-ec2e-4760-808b-6f3fa693e16e","resolution":{"observed_at":"2026-05-25T03:26:36.291031Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Cross- modal alignment and translation for missing modality ac- tion recognition.Computer Vision and Image Understand- ing, 236:103805","venue":null,"work_id":"e545bc54-1dd0-4170-94d0-54fd355338aa","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:73f8364287ebfe14b800dee6510f9d95ffe0de485793a51e0cfacbbd300e44b0","observation_id":"15f1c25e-7d3d-42fa-b016-a7ca52f33d71","resolution":{"observed_at":"2026-05-25T03:26:36.246715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Balanced meta-softmax for long- tailed visual recognition.Advances in neural information processing systems, 33:4175–4186","venue":null,"work_id":"e2abc89c-9f4f-4421-985f-503ad9cafbb1","year":2020},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:b873771dec4e43cbb968baff442b728cf75481e39a364766487070bea8eb016c","observation_id":"4751828d-ab0a-4f97-984d-15d7463f9195","resolution":{"observed_at":"2026-05-25T03:26:36.293673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Cytran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast ct translation.Neurocomputing, 538:126211","venue":null,"work_id":"2e2c978f-c506-487c-86fa-e4c2c9d64e31","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:00f16cc2beec8741da50bf72418f28893cb1c484a5b1a8f02b2057c0bc443526","observation_id":"22aec4b0-2cc4-4827-8dc5-38410409501a","resolution":{"observed_at":"2026-05-25T03:26:36.297466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Beyond invasive biopsies: us- ing vasari mri features to predict grade and molecular pa- rameters in gliomas.Cancer Imaging, 24(1):3","venue":null,"work_id":"d7c45bb5-03c1-4260-acf1-d172527ced0f","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:c72039d2ef4fdcbe5bca02cd4f7c9b81b6a0517690cfa44c76f06a1fffddba4c","observation_id":"1285e2ba-0667-43a5-bada-5f019e85b719","resolution":{"observed_at":"2026-05-25T03:26:36.267178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Variational mixture-of-experts autoencoders for multi- modal deep generative models.Advances in neural infor- mation processing systems, 32","venue":null,"work_id":"7f62c5a0-76bd-4e75-9928-67e33e5f3ff3","year":2019},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:0c696adbb4554e117cd407b829c75f62a9a27325d19ce15f9b4ca441aecc727f","observation_id":"2a4d1083-87f9-46b7-9be7-d0ce9756b2e8","resolution":{"observed_at":"2026-05-25T03:26:36.237066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Passion: Towards effective incomplete multi-modal medical image segmen- tation with imbalanced missing rates","venue":null,"work_id":"f1c3a6b4-7acd-4270-b518-a0366cf439a7","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:d9ca79ae1ba87465902e6e2d58aa05c85378640fae2f7a538b87f89ef31649de","observation_id":"856f1ca2-3568-4611-9eb5-4bfa65230829","resolution":{"observed_at":"2026-05-25T03:26:36.235333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Glioma subtype prediction based on ra- diomics of tumor and peritumoral edema under automatic segmentation.Scientific Reports, 14(1):27471","venue":null,"work_id":"f8aa1cf3-e097-489a-b051-2f1e991fac53","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:be0f060642f5b46fb025462ec83c70083b2d7caf8e9359c52f013c76f701d336","observation_id":"da391231-c137-44e8-b42d-dce1c7871835","resolution":{"observed_at":"2026-05-25T03:26:36.277198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Self-supervised pre-training of swin transformers for 3d medical image analysis","venue":null,"work_id":"1da79300-0174-4620-b3d7-8c673767c525","year":2022},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:08a7aa38530bd6eac9a4afb3fb6d4f5a8c86ade1f9adc93a9d008cf24a3572e1","observation_id":"d30ed9c2-27a4-4fa0-b453-fc59ca87de60","resolution":{"observed_at":"2026-05-25T03:26:36.281074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.Neuro-oncology, 25(2):279–289","venue":null,"work_id":"97554adb-69e2-423e-bb40-53927665861b","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:d1136f6bc3d9adc9a2c09caea862cbeb02368f9336557921e3edb639837a63f8","observation_id":"9e03f4ae-931f-43bc-89c8-bfc476dbbfc8","resolution":{"observed_at":"2026-05-25T03:26:36.294195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-07-07T10:13:40.911049Z","title":"Attention is all you need.Advances in neural information processing systems, 30","venue":null,"work_id":"ce925c33-3953-4a3a-bec2-824c6d3446fe","year":2017},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:4416d2014e411ec7b469a2cafe5e3f75dfaf13908d935e2af5d29916eb6f2ddf","observation_id":"f5c072f1-8fc4-4564-8919-5318dffa8bdd","resolution":{"observed_at":"2026-05-25T03:26:36.321897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"T5-based model for abstractive summariza- tion: A semi-supervised learning approach with consis- tency loss functions.Applied Sciences, 13(12):7111","venue":null,"work_id":"d1fac49c-aafd-4a3a-a979-1ad82216a9be","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:3bf9a9e3680b9b58994c0d3fa91e515bc9f2241c772e36db25453e29f39f3c81","observation_id":"004b14ae-4081-412d-a6fe-64f9a83571c2","resolution":{"observed_at":"2026-05-25T03:26:36.223651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Swin transformer improves the idh mutation status prediction of gliomas free of mri-based tumor segmentation.Journal of Clini- cal Medicine, 11(15):4625","venue":null,"work_id":"470fdb54-81fa-4f9d-95c9-86ca005cd6da","year":2022},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:a29fe4e85c6bf69b534e60af4a14166bb65e635573c9afe4cca6cf070cb9df7a","observation_id":"10df4a18-7389-4f86-9b0c-33aa0f4d682b","resolution":{"observed_at":"2026-05-25T03:26:36.218782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma pa- tients.NPJ Precision Oncology, 8(1):181","venue":null,"work_id":"222c3b8c-2944-4621-9193-36ee7deb92b8","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:3aa41f1e93a3eed70cdd2be578237cadfedf9e5772198c6d75fbc26b51f00573","observation_id":"cce45853-84d9-4d18-bca4-27eed88b243a","resolution":{"observed_at":"2026-05-25T03:26:36.227311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Rethinking masked image modelling for medical image representation.Medi- cal Image Analysis, 98:103304","venue":null,"work_id":"de782b0c-8489-4860-acb4-63d65901e4e9","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:3c6a883be598c22f356d611520cedb6a80bdbc2ec06752cf105d760f0d1f52e9","observation_id":"1e38cd6b-8d9a-4f9c-96ba-14d2bdc323d7","resolution":{"observed_at":"2026-05-25T03:26:36.205723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Leveraging knowledge of modality experts for in- complete multimodal learning","venue":null,"work_id":"4a836af1-2e9e-42f7-b513-6bd573e84826","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:da5c79437ec123dd0d357d1c137fe5f61c9681480937a256e79062aa7b181e54","observation_id":"59345623-915f-41f2-9d88-87fb5d491986","resolution":{"observed_at":"2026-05-25T03:26:36.208064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.17397","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T19:08:50.032692Z","title":"Mcmoe: Complet- ing missing modalities with mixture of experts for incom- plete multimodal action quality assessment.arXiv preprint arXiv:2511.17397","venue":null,"work_id":"f3dc4491-42e1-4f87-98b7-895285145ea5","year":2025},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:3bcfbffdf54d21696986ecbd0203abca3c4961e405c47d4768760fcc4d754338","observation_id":"5437aff4-f5f1-43ff-9ab9-5fd7149411b4","resolution":{"observed_at":"2026-05-25T03:26:35.624604Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Predicting the molecular subtypes of 2021 who grade 4 glioma by a mul- tiparametric mri-based machine learning model.BMC cancer, 25(1):1171","venue":null,"work_id":"dabf9df7-6bf8-4628-83e4-10795997f10a","year":2021},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:239e8f975edf0fdfeeaed666e0195db7bcf11c6be9139f009053510794daaec6","observation_id":"12af5f97-8ffc-4a82-ad80-07b119b06abf","resolution":{"observed_at":"2026-05-25T03:26:36.240175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Gain: Missing data imputation using gen- erative adversarial nets","venue":null,"work_id":"421f33be-aab1-47db-864d-f05ae8fc9129","year":2018},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:0000e27c2d70fcbdc120aa01f58b4a02c6872dd6a38fbbb2859f34d1eb470ea2","observation_id":"3fa57bb1-907f-4c4f-9330-5c58cdea92b2","resolution":{"observed_at":"2026-05-25T03:26:36.287285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.07294","last_updated":"2018-03-20T08:33:20Z","snapshot_observed_at":"2026-07-06T06:29:11.157250Z","submitted_at":"2018-03-20T08:33:20Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","version":1},"cited_work":{"arxiv_id":"1803.07294","doi":null,"metadata_source":"pith","pith_arxiv_id":"1803.07294","snapshot_observed_at":"2026-07-11T01:47:47.693612Z","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","venue":"cs.LG","work_id":"10d81639-1b59-491e-80fd-dcf342e3f6fb","year":2018},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"cited_paper":"/paper/1803.07294","citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:8e678fb17dd6c070c72d21b40c81f1533191982ba750220a7253e92c2e4c3d57","observation_id":"3180a4fc-c121-4701-8fbb-81092a451f93","resolution":{"observed_at":"2026-05-25T03:26:35.633233Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Deep long-tailed learn- ing: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816","venue":null,"work_id":"8892cb1e-e59a-4960-9751-86c2d70348d9","year":2023},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:84571b8189771c6ecb8d43de999dedb4108957404bdad0ff517f1359754906f6","observation_id":"d40f1dd8-0aec-408a-9b67-b4777dea6743","resolution":{"observed_at":"2026-05-25T03:26:36.214909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-06-05T21:23:00.469572Z","title":"Deep learning-based reconstruction on intensity-inhomogeneous diffusion magnetic resonance imaging.Iradiology, 2(6):571–583","venue":null,"work_id":"cd026fdc-b909-415e-9a93-370ffb144306","year":2024},"citing_paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-25T03:25:44.104161Z"},"links":{"citing_paper":"/paper/2605.23183"},"observation_digest":"sha256:59363b0fa1a5d8b291168aefaada1c9cb8d2bc5b2e18fa070f97aaf4f54348f4","observation_id":"709e2f9e-2fdc-4bf2-943f-1bb1f3472444","resolution":{"observed_at":"2026-05-25T03:26:36.211722Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.23183","last_updated":"2026-05-22T03:05:34Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-12T07:54:08.497303Z","submitted_at":"2026-05-22T03:05:34Z","title":"GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":3,"verified_fuzzy":36},"total_outbound_references":39},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2605.23183."}