{"as_of":"2026-08-17T13:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4220b0d035876432b1a6d8a087c4214b83e8bef056293fd0475b705f23580c18","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T20:47:02.391684Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:13:48.590115Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T17:18:14.737755Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11997","snapshot_observed_at":"2026-08-06T23:13:48.590115Z","title":"arXiv preprint arXiv:2505.11997 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.19324","last_updated":"2025-06-24T05:31:13Z","snapshot_observed_at":"2026-08-12T18:00:57.551564Z","submitted_at":"2025-06-24T05:31:13Z","title":"Memory-Augmented Incomplete Multimodal Survival Prediction via Cross-Slide and Gene-Attentive Hypergraph Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:13:48.590115Z"},"links":{"cited_paper":"/paper/2505.11997","citing_paper":"/paper/2506.19324"},"observation_digest":"sha256:23fcf0962d646e120425fcbd84a94447121b06ccacfa1d821c34c0395ffde3b6","observation_id":"0d87edc5-1540-48de-a0ab-6000dc28d7e7","resolution":{"observed_at":"2026-08-06T23:13:48.590115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"cited_work":{"arxiv_id":"2505.11997","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.11997","snapshot_observed_at":"2026-08-05T17:18:14.737755Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","venue":"cs.CV","work_id":"7b47d565-0b9a-4931-b7b5-4ac2037cb9d4","year":2025},"citing_paper":{"arxiv_id":"2508.16487","last_updated":"2025-08-22T16:02:06Z","snapshot_observed_at":"2026-08-05T17:18:12.741392Z","submitted_at":"2025-08-22T16:02:06Z","title":"FraPPE: Fast and Efficient Preference-based Pure Exploration","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T17:18:14.564171Z"},"links":{"cited_paper":"/paper/2505.11997","citing_paper":"/paper/2508.16487"},"observation_digest":"sha256:b37e52138e771b52a12a717b62a4850fad2d0fe6a2e4af10f7572f9885a52907","observation_id":"1c3d6842-c707-4af1-af06-891bb3af9d31","resolution":{"observed_at":"2026-08-05T17:18:14.740811Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11997","snapshot_observed_at":"2026-08-03T20:52:39.605256Z","title":"Multimodal cancer survival analysis via hypergraph learning with cross-modality rebal- ance.arXiv preprint arXiv:2505.11997, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.18089","last_updated":"2026-07-14T11:29:30Z","snapshot_observed_at":"2026-08-13T08:27:20.147073Z","submitted_at":"2025-11-22T15:10:46Z","title":"Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T20:52:39.605256Z"},"links":{"cited_paper":"/paper/2505.11997","citing_paper":"/paper/2511.18089"},"observation_digest":"sha256:57457b24cce33908270f1d9c7acbafee1c0d833bff680335d01252e0d74b681e","observation_id":"1ba743f1-27f6-445f-9194-8e3464cdbefd","resolution":{"observed_at":"2026-08-03T20:52:39.605256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.11997/citation-record","integrity":"/paper/2505.11997/integrity","json":"/paper/2505.11997/citation-record.json","paper":"/paper/2505.11997"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:47:03.057883Z","title":"Representation learning of histopathology images using graph neural networks","venue":null,"work_id":"4a9c0597-a316-486c-830e-6b7b138db105","year":2020},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.177162Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:195924ad863fa82ef3f45a1463ae799dab1e8cc5c1e17fcb340d7a4aa0ea15e0","observation_id":"ad62e13d-cc42-449d-98ce-b8584c61aa8e","resolution":{"observed_at":"2026-08-15T20:47:03.061863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.980013Z","title":"Pathomic fusion: an integrated framework for fusing histopathology and genomic features for cancer diagnosis and prognosis","venue":null,"work_id":"280b1373-6328-4920-808c-2be074117316","year":2020},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.208140Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:08406705aec951b85458e5d1bd8dfd96118e6fafac9263fb05d177a96f843787","observation_id":"31b5a157-d588-4f9c-8b37-515bc6fbe2d0","resolution":{"observed_at":"2026-08-15T20:47:02.984607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.954748Z","title":"Towards a general-purpose foundation model for computational pathology","venue":null,"work_id":"3093a283-d86a-4a85-aafd-9482ce5c9fd0","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.217142Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:c60430d0ee2a31e7199a3918354f433f318c89f7ec29b0216e71c007415709d4","observation_id":"75a4903f-bb0b-49c9-89d4-2d3f2447b689","resolution":{"observed_at":"2026-08-15T20:47:02.958897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.941458Z","title":"Ad- dressing failure prediction by learning model confidence","venue":null,"work_id":"07084dc3-c98f-417d-bbcf-570c1660d8bd","year":2019},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.221962Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:d265c7c6dd3ae3234e6e137f528d8555d595250768c2d6a90dc4e5e243cf4798","observation_id":"e59aa706-6157-48b4-8fbe-c7471a7a5e52","resolution":{"observed_at":"2026-08-15T20:47:02.945871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.927355Z","title":"Solving the multiple instance problem with axis-parallel rectangles","venue":null,"work_id":"24658bfa-4bf2-4d6c-a055-d73fc23c32e5","year":1997},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.227409Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:4127718c326f453ae61a20beb1cece6579f2497191db240cddbfeddab1801483","observation_id":"aa339353-f0e0-48c2-8a3a-d98851e3b90f","resolution":{"observed_at":"2026-08-15T20:47:02.932558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.899529Z","title":"Learning visual features by colorization for slide-consistent survival prediction from whole slide images","venue":null,"work_id":"557e7d0c-e51b-44d7-814b-0426a0afb0fd","year":2021},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.236227Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:40b9db837732b90cdb6304712bc583868d53ab414ef3727ff5ed2b4e7bc5e7a1","observation_id":"430f757c-a899-45f6-8fad-e354b5b601f0","resolution":{"observed_at":"2026-08-15T20:47:02.903818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.886153Z","title":"Hypergraph neural net- works","venue":null,"work_id":"5d99e210-cf67-4b95-8464-3cc296fd0b1c","year":2019},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.240582Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:2ae95679d86d734e993fe382c4769b4e4018483a69b06c9715e5ae1ed21a7c32","observation_id":"149043a3-a249-4904-a640-6836f2ce729c","resolution":{"observed_at":"2026-08-15T20:47:02.890123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.871791Z","title":"Scaling self-supervised learning for histopathology with masked image modeling","venue":null,"work_id":"36089c24-b5cc-4975-b08f-41ad9654247d","year":2023},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.245139Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:c6b186d3951b50f5189279f93e5486d0930effdfe65b31cc4b80bf632c7ad55b","observation_id":"540a4eae-79e3-4c91-9bdb-022b787653e6","resolution":{"observed_at":"2026-08-15T20:47:02.876921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.858912Z","title":"Node-aligned graph convolutional network for whole-slide image representation and classification","venue":null,"work_id":"d3d9af8d-b412-429d-967c-241ef0bc40ff","year":2022},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.249048Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:d61cfe1e9fbc6800b26467993b83c98608fdc665ca0fd9c89ab88f0dc2c0efa3","observation_id":"114f3308-0e62-4078-89a9-cdad672cb325","resolution":{"observed_at":"2026-08-15T20:47:02.863487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:47:02.253318Z","title":"Neural networks: a compre- hensive foundation","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.253318Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:efc49177bda6713230ceec2efaa60577388e55f35980e520e5c8116daec2535a","observation_id":"7ce8a9d3-9865-440a-816f-c7b3fdc943ae","resolution":{"observed_at":"2026-08-15T20:47:02.253318Z","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":"2026-08-15T20:47:02.809115Z","title":"Hˆ 2-mil: exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis","venue":null,"work_id":"5556b21d-efb2-4007-a5d6-6d58ab8c5311","year":2022},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.266984Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:5ae530e908984422bd7db8973bc2c385e3016ea954d0d7883eb78b891f175571","observation_id":"5062ca74-9592-4d38-a61c-f267f922abbf","resolution":{"observed_at":"2026-08-15T20:47:02.813823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.790910Z","title":"Attention-based deep multiple instance learning","venue":null,"work_id":"70a2a227-423f-4794-a461-578e218d0733","year":2018},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.271115Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:e4d0c994df2786651a8368969f98c5a96f5c95af6ddd0f2a394645265c9b37a6","observation_id":"bc3eebc2-5961-4878-9595-7ced064997ca","resolution":{"observed_at":"2026-08-15T20:47:02.796192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.762283Z","title":"Hmil: Hierarchical multi-instance learning for fine- grained whole slide image classification","venue":null,"work_id":"3d997298-c26a-4662-8c81-aca3a639e91d","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.280206Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:1bac5d7a80e60dffc5d9fd972ada3a24a1d05766a91aba7c5b05a939e0d8d22c","observation_id":"e84e6af8-6238-43d8-a6d8-4f542bb7331c","resolution":{"observed_at":"2026-08-15T20:47:02.767987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.741603Z","title":"Multi-modal hy- pergraph contrastive learning for medical image segmen- tation","venue":null,"work_id":"fb5aca84-4421-4a84-8b53-9f9d12b24108","year":2025},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.284208Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:a3dad617a69fa895fe9c0a26af7b4eaacac7071a9359a0387b4a2d9855fb2d28","observation_id":"61e807ee-3c32-478c-934a-92628b06f3f9","resolution":{"observed_at":"2026-08-15T20:47:02.747990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.706345Z","title":"Self- normalizing neural networks","venue":null,"work_id":"5503b9fc-662c-45b9-955a-5f8d095f3986","year":2017},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.294901Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:9bcdb3cc928ff0252378edaa04a03ce9cdfcd42f255509cfe665834c26ec1680","observation_id":"739b5ffc-1d3a-42d4-824a-d0a961a39563","resolution":{"observed_at":"2026-08-15T20:47:02.711874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.676708Z","title":"High-order correlation-guided slide-level histology retrieval with self-supervised hash- ing","venue":null,"work_id":"0923392c-c033-43d9-b4b5-a54e804978bf","year":2023},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.304685Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:3b508ebdcfb809fbbe4eb73e6fce213f18560eb6f5714fff711367618be300d4","observation_id":"a037bb20-b74c-44da-af3f-83e8ef7bb4ea","resolution":{"observed_at":"2026-08-15T20:47:02.680742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.664385Z","title":"Artificial intelligence for multimodal data integration in oncology","venue":null,"work_id":"738f03f0-a657-4f9e-be48-82333c44a6c4","year":2022},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.308730Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:5f870ebb4ed4b03cafe84fc467dd32f308b0155fa01f3f755b491eb72d35f373","observation_id":"36b086c8-991e-49f1-a038-ee9bed4fb359","resolution":{"observed_at":"2026-08-15T20:47:02.668394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.652436Z","title":"Data-efficient and weakly super- vised computational pathology on whole-slide images","venue":null,"work_id":"b57dabad-af91-4f17-b664-a57ed5bf08ad","year":2021},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.314414Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:9fda3c4643ba63b311e1ff8b29a2e6ed022588e5309500d55a433d1821c1457a","observation_id":"b1527fde-391a-411a-bed4-254950fa7bf9","resolution":{"observed_at":"2026-08-15T20:47:02.656543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.639883Z","title":"A visual-language foundation model for computational pathology","venue":null,"work_id":"4ef3f0ab-e85d-4d8c-a4a0-43997824c8b8","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.319176Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:a2d4d342a8201403dec9f7ae34035db33a3750c1811975862bfe2cbccb814a18","observation_id":"eccc67bc-0d51-4f0b-9c9a-33a28adffa5e","resolution":{"observed_at":"2026-08-15T20:47:02.644742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.626944Z","title":"Predicting cancer out- comes from histology and genomics using convolutional networks","venue":null,"work_id":"8a195687-5b4d-41fb-8be3-cd995062f8ee","year":2018},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.324519Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:1c237dd1234ef71aa7a263f720e1c4d92355cb41eefe4848cafc5b13c1ce35a8","observation_id":"f5c5156f-3829-40d9-94b3-ae53f4eaee19","resolution":{"observed_at":"2026-08-15T20:47:02.631041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.614470Z","title":"Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images","venue":null,"work_id":"160b7970-d1a8-4dc0-b6d6-b81f47798e06","year":2023},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.328847Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:cbb04e029e13857e0e4a2bd5649752725136d506e02cbfb7c286762f8811d8c8","observation_id":"73cface3-60da-4236-8a3f-16369936ac3e","resolution":{"observed_at":"2026-08-15T20:47:02.618921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.601343Z","title":"Prognostic genome and transcriptome signatures in colorectal cancers","venue":null,"work_id":"fbaf2328-2c35-4749-8216-558841c0b1ae","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.333798Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:abd91582497ab1b84c0ab41e80cc4434c89f25f744cceceb11fb583dffd30c74","observation_id":"040cbad1-461b-4b83-a4ea-1a00b5a3ccd1","resolution":{"observed_at":"2026-08-15T20:47:02.605631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.584577Z","title":"Boundary-guided learning for gene expression prediction in spatial transcriptomics","venue":null,"work_id":"a6e1ab24-587f-4286-a474-984d4648959a","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.338470Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:f86dc5861205c8b21fb3f0596c3be0bec5b0de216781ddf5577769ca00312df8","observation_id":"a7a1bb81-64fa-4ad2-a995-8f18c2f89a46","resolution":{"observed_at":"2026-08-15T20:47:02.589027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.571748Z","title":"Noise in gene expression: origins, consequences, and control","venue":null,"work_id":"2d1ac162-bb0d-46ed-b37b-e00fb0f4ae6f","year":2005},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.344049Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:803e9b9f688904e337b4584677897020f83a19e795dd3b836031c229f485258b","observation_id":"adac73f9-ff2a-4d2c-8d2b-42ac1c3fcc2d","resolution":{"observed_at":"2026-08-15T20:47:02.576322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.544119Z","title":"Tumor micro- environment interactions guided graph learning for sur- vival analysis of human cancers from whole-slide patho- logical images","venue":null,"work_id":"d97b068d-740d-4f99-b3d0-336a2a3baddb","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.352293Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:18e91b0f128a4efc97134e60826debc8c36e8ce0be8f1f40950127eb71c60b63","observation_id":"b273e432-4f0f-4e01-a204-81b8a9a54f14","resolution":{"observed_at":"2026-08-15T20:47:02.548424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.12068","last_updated":"2025-03-15T09:55:31Z","snapshot_observed_at":"2026-08-16T12:49:47.577020Z","submitted_at":"2025-03-15T09:55:31Z","title":"Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.12068","snapshot_observed_at":"2026-08-15T20:47:02.356514Z","title":"Prototype-based image prompting for weakly supervised histopathological image segmenta- tion","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.356514Z"},"links":{"cited_paper":"/paper/2503.12068","citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:a9fdd7c66597a956a7602ddd036f03c61c8618518632e159ce5925b7a09fb625","observation_id":"7f319f44-d810-473b-8c90-3bdaf6bf3ef9","resolution":{"observed_at":"2026-08-15T20:47:02.356514Z","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":"2026-08-15T20:47:02.530486Z","title":"Transformer-based unsupervised con- trastive learning for histopathological image classification","venue":null,"work_id":"d4ed301e-e10d-464d-9132-430b71a63b67","year":2022},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.361736Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:efd93d245f69211a677025297c90bfa6c1a1b524d6b45f865fee84db9f6ef530","observation_id":"ce84c040-4829-44aa-8d98-76328d656b1b","resolution":{"observed_at":"2026-08-15T20:47:02.535084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.514730Z","title":"Multi- modal optimal transport-based co-attention transformer with global structure consistency for survival prediction","venue":null,"work_id":"35fd5cd5-404c-4ee7-b347-73d6331ec6ad","year":2023},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.365645Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:24a713ac59391fd6cf579e94d1b08d854c2612e3ab64b26ea60fbe8e1945d26b","observation_id":"3c284dfa-d362-40c4-bf74-fea127c6e939","resolution":{"observed_at":"2026-08-15T20:47:02.519987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.499755Z","title":"Whole slide images based cancer survival prediction using at- tention guided deep multiple instance learning networks","venue":null,"work_id":"ddc2cb32-1579-435f-94f1-02fe7be65374","year":2020},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.369501Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:b7fc65d79255c403f0009abfb6d48a807df8e842b88e9ca8f6847f2a70919595","observation_id":"fec01d5b-e952-4e56-ab47-1dbba0ee7806","resolution":{"observed_at":"2026-08-15T20:47:02.504650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.485138Z","title":null,"venue":null,"work_id":"cab411e0-d204-4d62-8719-24a3dcc0b957","year":2021},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.373173Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:b13b202af865af35af91538733bcc08b940f772987f5a340315778a0f4b6bf32","observation_id":"2f101efa-8228-49d9-8010-eea70a4737ac","resolution":{"observed_at":"2026-08-15T20:47:02.489029Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.472607Z","title":"Prototypical information bottlenecking and disentangling for multimodal cancer survival prediction","venue":null,"work_id":"1c669cbc-e747-4a48-b699-d23255bf8681","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.378349Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:8a4cfd6783e056ae8c48be2c88c2359196000385dc8b036ae3e5f65b51035cf2","observation_id":"606c5e97-136c-4e33-8c50-e6f657690965","resolution":{"observed_at":"2026-08-15T20:47:02.476778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.459112Z","title":"Histopathological whole slide image analysis using context-based cbir","venue":null,"work_id":"8f219553-5795-4be2-b25a-ca9f17321fc9","year":2018},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.383885Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:2d5576afafafdc946277a853f83c25332ed37a853823486fbc64d769882b91a7","observation_id":"24588990-d480-4b6b-92f0-4574594c9e5f","resolution":{"observed_at":"2026-08-15T20:47:02.463734Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.442729Z","title":"Cross- modal translation and alignment for survival analysis","venue":null,"work_id":"78e97bd8-06f8-4b6d-b592-7135a597d6c9","year":2023},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.391684Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:b56f0eeb34315d80f2515e2691bd063b7cbd6f88b8700f50dfafa106c14c0751","observation_id":"1e924a79-3ccb-42a0-b5ea-45e49166db17","resolution":{"observed_at":"2026-08-15T20:47:02.448679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.912898Z","title":"Sheaf hypergraph networks","venue":null,"work_id":"befc1a5b-19ab-4ebd-8ed4-79ec444a1358","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":1997,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.231999Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:9a004b0152b4f46f510730c2e01e28c5dea4fec85eae8d753bf1dd7eeaaddc38","observation_id":"6729a54e-027c-44c4-9483-7cb33759eed7","resolution":{"observed_at":"2026-08-15T20:47:02.916957Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T20:47:02.257676Z","title":"Deep residual learning for image recog- nition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":1998,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.257676Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:d40d50fe9ccb8d91b846bf3e032027602b44ae5e411ee92631f05bdd872fdc8e","observation_id":"769cf54e-d13f-4445-be06-779647f7ae94","resolution":{"observed_at":"2026-08-15T20:47:02.257676Z","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":"2026-08-15T20:47:02.556961Z","title":"Transmil: Transformer based correlated multi- ple instance learning for whole slide image classification","venue":null,"work_id":"006f749b-c9a5-4a16-bc36-e3ab7030948a","year":2021},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2005,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.348072Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:e55e14cf8855623af01a662916942c1ca858993b8d6afcf20472d0d2d2e61c6e","observation_id":"73a842b3-9545-474b-a343-f9b784eb39b5","resolution":{"observed_at":"2026-08-15T20:47:02.562283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.827186Z","title":"Patch- based convolutional neural network for whole slide tissue image classification","venue":null,"work_id":"98074384-ccc1-4198-863b-63b5cb4227c6","year":2016},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.263072Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:3aef4c080c44b538bfcb1251af0a413574e6812dbcdefea69b7321253fbde0b7","observation_id":"4ece9b36-536c-4608-be76-8041ee3ba54d","resolution":{"observed_at":"2026-08-15T20:47:02.831822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.689833Z","title":"Dual- stream multiple instance learning network for whole slide image classification with self-supervised contrastive learn- ing","venue":null,"work_id":"10834401-cc24-491c-80e8-88225cc9c365","year":2021},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.299507Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:a076b4e07105d87f28d47b8efe8b30ae68ce1ef227f208aa30e944ffc7fd563e","observation_id":"cfb18776-a8d0-4a34-91c4-d53de482348c","resolution":{"observed_at":"2026-08-15T20:47:02.695707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.776759Z","title":"Modeling dense multimodal in- teractions between biological pathways and histology for survival prediction","venue":null,"work_id":"7e58a51b-9687-4321-ab10-c242ddfd19e1","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.275597Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:327365360cf43a693c84e84fcd30c4f0024d8efd779b760fcc27012a22fa9692","observation_id":"c9c085b7-4a94-476d-8711-e0d57dcf15ab","resolution":{"observed_at":"2026-08-15T20:47:02.781337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:03.020343Z","title":"Predictive dynamic fusion","venue":null,"work_id":"1619872b-34b5-4dc0-9198-d536061f3725","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.192728Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:2a64a7ec37d9d9da6a94cc4a328b41fab532367a7b21ea7e69ba8f2f11f4bc55","observation_id":"5cc33df1-9f49-4c2f-ac05-5bd18ceb6f1e","resolution":{"observed_at":"2026-08-15T20:47:03.025032Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:03.045564Z","title":"Genomics to select treatment for patients with metastatic breast cancer.Nature, 610(7931):343–348,","venue":null,"work_id":"edf5027f-5440-4b09-bed5-60ffa1afc890","year":2022},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.182254Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:b96bff4e0992b6ca6bc4f447900b0972a53383893d8dc02894f2faf943a72ca1","observation_id":"362e9b57-b1ae-4088-970e-71d6e9d2a15a","resolution":{"observed_at":"2026-08-15T20:47:03.050023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.966891Z","title":"Pan-cancer integrative histology- genomic analysis via multimodal deep learning","venue":null,"work_id":"eeaf5e3c-0904-4887-a254-2c1e9fdb2ba9","year":2022},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.212885Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:ce3d559e08c788f35b14fde248ebee85e87f0f49aeb09a436fa46034f3169a1a","observation_id":"f19a8ee4-b51f-44b5-b183-c5dda185bdc4","resolution":{"observed_at":"2026-08-15T20:47:02.971294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:03.033030Z","title":"Clinical-grade computa- tional pathology using weakly supervised deep learning on whole slide images","venue":null,"work_id":"1741d812-8b43-4241-b95e-85cd4929487e","year":2019},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.187041Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:cd23085f01616402b0bc77b8e49780e47e2e56ef4e3146fa02116e1c8f595511","observation_id":"c19aeb91-bb2c-4fab-8891-56f82d21d061","resolution":{"observed_at":"2026-08-15T20:47:03.037449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.993705Z","title":"Deep learning with multimodal representation for pancancer prognosis prediction","venue":null,"work_id":"717ba32e-b9fb-4979-96c3-56fe8b25302c","year":2019},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.202664Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:9d42297d2b4bf7cae57386ae70c961c108fc28ca3b50bd887b4e7631ccfc43e1","observation_id":"05f57e6b-42ad-4940-9275-bc2c1809a18e","resolution":{"observed_at":"2026-08-15T20:47:02.998054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:03.008318Z","title":"Histopathology whole slide image analysis with heterogeneous graph rep- resentation learning","venue":null,"work_id":"6cfb207b-4416-4d54-bd26-b1d6cd3fa132","year":2023},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.197484Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:71f99d64ef266455fec97938d33be29b684765c1b994e42d7de229dcb1f97b92","observation_id":"4555904a-4fb9-4bb8-a4f4-58f93272f642","resolution":{"observed_at":"2026-08-15T20:47:03.012581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T20:47:02.721408Z","title":"Si-mil: Taming deep mil for self-interpretability in gigapixel histopathology","venue":null,"work_id":"a2c5ba5b-acb1-4d20-a8fb-2ac4c26b079f","year":2024},"citing_paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-15T20:47:02.289724Z"},"links":{"citing_paper":"/paper/2505.11997"},"observation_digest":"sha256:841eae6a25616c5bcc45771d900fef26f0b75ee6930fcb12d437295318f8a202","observation_id":"86580b71-e372-49f6-96b9-a88381e799cf","resolution":{"observed_at":"2026-08-15T20:47:02.727843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.11997","last_updated":"2025-05-20T11:04:32Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T20:41:14.451773Z","submitted_at":"2025-05-17T13:16:54Z","title":"Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":42},"total_outbound_references":46},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 3 inbound Pith citation observations for arXiv:2505.11997."}