{"as_of":"2026-08-09T06:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3320c0e4368f88a1eb64a1f76d593fdd5895099f91183550f5bad0a751e6f185","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:58:55.939101Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.03408/citation-record","integrity":"/paper/2509.03408/integrity","json":"/paper/2509.03408/citation-record.json","paper":"/paper/2509.03408"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.341914Z","title":"Representation learning of histopathology images using graph neural networks","venue":null,"work_id":"d5f04b39-306b-43e6-b097-1ee1a752054e","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:50.657816Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:640015b4a9fb44affd83aa727fb7ee0ca78cd27ea6dac09ecf8cf2b605cd8cf4","observation_id":"3242a8d1-ecc6-46c0-bdb7-815a855a09d1","resolution":{"observed_at":"2026-08-05T10:59:01.345236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.333029Z","title":"Estrogen and progesterone receptor testing in breast cancer: Asco/cap guideline update","venue":null,"work_id":"784e4cb0-55e7-412b-a023-d435dc0a5113","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:50.756520Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:13890c470e37432e2b069f8b2413f90456798105e53873794991c109997d2ff1","observation_id":"9262b7ca-1bb4-4003-9ac4-12eb100769d1","resolution":{"observed_at":"2026-08-05T10:59:01.336084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.323022Z","title":"Structured crowdsourcing enables convolutional segmentation of histology images","venue":null,"work_id":"a3a7ba1e-dcf4-435c-b953-c42851bf5847","year":2019},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:50.884379Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:1699a0187de77506263af4187c44b7cb568e919286537805ece1e74c335e7bc2","observation_id":"08de668a-c72f-4420-88e6-f925d1541629","resolution":{"observed_at":"2026-08-05T10:59:01.326105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.314592Z","title":"Big-graph: Brain imaging genetics by graph neural network","venue":null,"work_id":"aca78b28-03e5-4742-bb2b-eba7f19915dc","year":2022},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:50.936265Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:b4927dc93e266c586cc4bb949ddb02cd912d9353e3f6303f9035be01ee279aaa","observation_id":"29fcdfa2-83d0-4241-95a6-8841e5b2de1b","resolution":{"observed_at":"2026-08-05T10:59:01.317432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.305566Z","title":"Cell-graph mining for breast tissue modeling and classification","venue":null,"work_id":"cf4354b7-3f06-4de4-94de-7829402bceda","year":2007},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.038320Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:d0782d01283edf9656cc226d1338e3e4ab8860ea90b49a3a9001e990713526f3","observation_id":"5847bbb1-fdc4-4a38-b57a-bc2360853fa0","resolution":{"observed_at":"2026-08-05T10:59:01.308807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.295945Z","title":"Obesity and breast cancer: progress to understanding the relationship","venue":null,"work_id":"414cd903-8e99-4678-870b-81e051a15c7c","year":2010},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.109787Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:28123c2cfc89f65e47cc5b58611cbfce96d1230e04955162af68d2cd84f9dafd","observation_id":"f8820fc3-fe5c-4336-8002-7e278b5dce8f","resolution":{"observed_at":"2026-08-05T10:59:01.299723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.285569Z","title":null,"venue":null,"work_id":"99b01179-6987-463f-aee5-ae52fa2d0859","year":2009},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.239513Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:f9c5ae8c8936c2c65f6067e829b995ba77715e20e8947527d82e98110a4dc694","observation_id":"3c00022a-c75a-4e4c-9dc5-02030404117c","resolution":{"observed_at":"2026-08-05T10:59:01.289000Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.275206Z","title":"The cbio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data","venue":null,"work_id":"f9b96847-61a0-4a4e-9c7a-bd3786ffe2a3","year":2012},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.330168Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:7ab0c6ef76a9a3fa9ef2383c4a7a7658dd16b5e390a0593a5ebac87956ed3971","observation_id":"8ca15647-1cfc-4143-a051-25f6fffaff1f","resolution":{"observed_at":"2026-08-05T10:59:01.278843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.265198Z","title":"There is more than one kind of myofibroblast: analysis of cd34 expression in benign, in situ, and invasive breast lesions","venue":null,"work_id":"fccf13d5-14d4-42e4-9bc6-3c5402a7f599","year":2003},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.404668Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:c3b8a6302445015f43bdb8130597ede4f9282fb0adb6dea6c7d227359789c6ec","observation_id":"74bae4e1-9758-4b85-9ee0-9bf0153c0f0d","resolution":{"observed_at":"2026-08-05T10:59:01.268361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.255451Z","title":"Pan-cancer integrative histology-genomic analysis via multimodal deep learning","venue":null,"work_id":"3290f6c1-fb59-4d36-878a-2b1cac7e11c7","year":2022},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.460642Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:6a06918ae625727e2e6630db8114291bd471b22722c25435841d7a5cd7d32709","observation_id":"bf0aac7b-b253-40ee-b644-55b7c2f6c9fd","resolution":{"observed_at":"2026-08-05T10:59:01.258848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.245915Z","title":"Expression of cluster of differentiation 34 and vascular endothelial growth factor in breast cancer, and their prognostic significance","venue":null,"work_id":"728eb164-d994-426b-9894-00f14d196d23","year":2015},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.540130Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:c93d9c2c8e52a61ba8b59f42cf625530f1c07f7c78121d2504a445b8f83f3615","observation_id":"dd121f19-4346-4e0a-b0f3-443c866c85c9","resolution":{"observed_at":"2026-08-05T10:59:01.249190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.236011Z","title":"Diagnostic significance of the immunoexpression of cd34 and smooth muscle cell actin in benign and malignant tumors of the breast","venue":null,"work_id":"cd85182d-d29a-4af6-93b4-e1309d8ed42c","year":2005},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.606613Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:fef2c9ae976ddf4206f67f8304b78bc66170b98393dca92724420133e022e7f4","observation_id":"0a269213-5e34-4280-89a6-706fb563ad5f","resolution":{"observed_at":"2026-08-05T10:59:01.239601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.224959Z","title":"Principal neighbourhood aggregation for graph nets","venue":null,"work_id":"c2021f21-da6f-49e8-a659-9fb519356321","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.674903Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:4a10a283be3a81fb2bb5c2313fead41da4b3cb1104849ac774f314bfe6f71b97","observation_id":"1b250fa0-be85-4a82-8763-70df8c419ab0","resolution":{"observed_at":"2026-08-05T10:59:01.228646Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.215373Z","title":"Tumour heterogeneity and resistance to cancer therapies","venue":null,"work_id":"642163f9-3c53-4d8d-a2c4-6d7d753461bd","year":2018},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.803063Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:0df205bc6354b224b0387c38d0ffbb45953fd9557b7519da77483fc02de074e8","observation_id":"94b7711a-a6f3-40a1-afa1-f5655885d8e7","resolution":{"observed_at":"2026-08-05T10:59:01.218571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-05T10:58:51.900155Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:51.900155Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:a1a903ed8aeb2581a49486204840d4aaacc88b5f62c42d3d764fc63ef6f83559","observation_id":"80544cbb-0c34-4119-9500-f4e37de2b9a6","resolution":{"observed_at":"2026-08-05T10:58:51.900155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10778","last_updated":"2020-04-22T08:52:04Z","snapshot_observed_at":"2026-07-06T09:06:56.021993Z","submitted_at":"2020-03-24T11:25:12Z","title":"PanNuke Dataset Extension, Insights and Baselines","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10778","snapshot_observed_at":"2026-08-05T10:58:52.013013Z","title":"Pannuke dataset extension, insights and baselines","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.013013Z"},"links":{"cited_paper":"/paper/2003.10778","citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:46373c0dea9130fe10a955aeaf986e1642f74045dbe7922ec7f258954c889e65","observation_id":"cf930bb8-7da8-46bb-826e-2b9bfa534c38","resolution":{"observed_at":"2026-08-05T10:58:52.013013Z","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":"2026-08-05T10:59:01.204651Z","title":"Estrogen receptor status, determined by immunohistochemistry, as a predictor of the recurrence of stage i endometrial carcinoma","venue":null,"work_id":"aa5085aa-7d76-492c-87fe-d841da223ff0","year":2083},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.096488Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:236cc4e1b2e28c72cf2b02903939b27e4622d8e8749fa14d620db1f5875ada61","observation_id":"bf09d235-e8d4-4674-a405-969c835d5558","resolution":{"observed_at":"2026-08-05T10:59:01.208197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.194199Z","title":"Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images","venue":null,"work_id":"c2832d36-a888-436e-a8da-64ef0aff8710","year":2019},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.213298Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:caae3d5e0e0e2216c7e446cd9f5ff594cfe7f69d7e348a96d754d1e157719eef","observation_id":"c7e7d3d4-cdf2-4bb8-add1-49ca3694c397","resolution":{"observed_at":"2026-08-05T10:59:01.198147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:01.038788Z","title":"Multimodal fusion with deep neural networks for leveraging ct imaging and electronic health record: a case-study in pulmonary embolism detection","venue":null,"work_id":"fa0a1d30-f0f2-4c97-a674-9ccbcb839301","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.305834Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:af17fdd2ee85a1ced6630becba62d97231843d2a6dfa8e08def0730301aa0daf","observation_id":"fa472135-32f9-46cc-aac4-0d0f0f0b0791","resolution":{"observed_at":"2026-08-05T10:59:01.111262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.887336Z","title":"Self-normalizing neural networks","venue":null,"work_id":"c7553a6a-c83b-48df-acd1-b9e938c7f99c","year":2017},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.442699Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:10621e00b11164a74c2ae4174bb503e96f81a9c65ab520e7cd8b9abb2e15562e","observation_id":"fa460d83-0c4f-4ec0-b525-f2a69f88d7fa","resolution":{"observed_at":"2026-08-05T10:59:00.959126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.739260Z","title":"Topological feature extraction and visualization of whole slide images using graph neural networks","venue":null,"work_id":"723551ad-ac0f-4f97-833c-85d3f319cb1e","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.591047Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:00e48016df06076680fc44c2721f4c3a264e87b288704dd75d1b38b3eb3e9652","observation_id":"1482e2e5-7c9b-4dad-a460-486158c6a106","resolution":{"observed_at":"2026-08-05T10:59:00.804427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.725460Z","title":"Mmgk: Multimodality multiview graph representations and knowledge embedding for mild cognitive impairment diagnosis","venue":null,"work_id":"9b61bfa4-bb2e-43b0-a135-db7b98e8d6b5","year":2022},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.726636Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:49fe07684ff88281deea177e406522c7985cc1dbd35a1d63853651a7da6f5c71","observation_id":"489c1a33-0f6d-4c45-a945-68071c3151af","resolution":{"observed_at":"2026-08-05T10:59:00.728744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.715140Z","title":"A hybrid deep learning model for predicting molecular subtypes of human breast cancer using multimodal data","venue":null,"work_id":"20ae3af4-f2b1-45cb-932d-a87481802525","year":2022},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.868536Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:851ee49305094f41388577a344a947bcb36b132b6250a3256905b544f3a01683","observation_id":"32066283-c5a0-4d4a-a9df-6dbbd3190a60","resolution":{"observed_at":"2026-08-05T10:59:00.718283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-05T10:58:52.993612Z","title":"Roberta: A robustly optimized bert pretraining approach","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:52.993612Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:af3cdf8854255092e789dcf6fabc150a9e7e93aad84c334c157acb96fbdcd52f","observation_id":"13d115ab-0ef4-4e14-8f2a-ccaf790231c9","resolution":{"observed_at":"2026-08-05T10:58:52.993612Z","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":"2026-08-05T10:59:00.705337Z","title":"Feature driven local cell graph (fedeg): predicting overall survival in early stage lung cancer","venue":null,"work_id":"5ed3131e-726f-4aab-b248-d290948da17b","year":2018},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.136987Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:ca42c8578398e311b48381be7ddf97406a86c154c7ef2a2539a97ee2357ce1b3","observation_id":"7dfa64eb-57c3-4cd3-abe4-d6c92cb58495","resolution":{"observed_at":"2026-08-05T10:59:00.708485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.683810Z","title":"Data-efficient and weakly supervised computational pathology on whole-slide images","venue":null,"work_id":"b382e360-6ab1-4618-b2fc-7ce74e83722e","year":2021},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.256740Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:1c63783bb37de4b646de25a29bfe4c4b9ef4f139325e27b281da6167070db218","observation_id":"addba19f-22af-4f28-ac89-39528a767803","resolution":{"observed_at":"2026-08-05T10:59:00.698880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.600792Z","title":"Capturing cellular topology in multi-gigapixel pathology images","venue":null,"work_id":"4e5f8df1-47ff-4cd5-b70b-6cd0455e71ae","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.353055Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:7c81f20a48c90f2453f4b6507281ebca43f7c2f09595d461264da358b46c19c6","observation_id":"92527e86-3c7c-43e3-9e63-363496deff3c","resolution":{"observed_at":"2026-08-05T10:59:00.676225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.465480Z","title":"Slidegraph+: Whole slide image level graphs to predict her2 status in breast cancer","venue":null,"work_id":"f63748b8-bf23-4030-911f-a3002676072d","year":2022},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.472114Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:435926f29244fa294fe23b7bcbcd4068b33d3c2d7d592f4f798e74b8c9bc94dc","observation_id":"67a0dae2-ac91-4cfd-852c-bc2067e19d72","resolution":{"observed_at":"2026-08-05T10:59:00.524389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.330460Z","title":"Expression and methylation patterns partition luminal-a breast tumors into distinct prognostic subgroups","venue":null,"work_id":"c75115a5-69aa-4adf-acde-3961dcee7a1b","year":2016},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.575075Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:7b32b1d508f93a4cbaeceb2c76e752534ec35e54c77ff8d0bcbcacda7ca5c72a","observation_id":"ac4d2e59-6bdc-4e3e-96fc-6808d71aa348","resolution":{"observed_at":"2026-08-05T10:59:00.397515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-06T05:58:29.182448Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-05T10:58:53.672846Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.672846Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:799af65673b2ab463eb0fd21acf2e50742e92f414dcc76a3ad60cebf9bcb2bcc","observation_id":"1f2d6204-cf14-4c35-b947-bd4d014ed40e","resolution":{"observed_at":"2026-08-05T10:58:53.672846Z","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":"2026-08-05T10:59:00.167298Z","title":"Supervised risk predictor of breast cancer based on intrinsic subtypes","venue":null,"work_id":"3a073038-6c06-4863-8898-4716a504fd42","year":2009},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.843653Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:ccbb1ea20291d2417913f5ce3019751775346a1102cf26b17a1e52d253fcbce9","observation_id":"6e6a3671-a94c-4fab-9105-079a1c96d859","resolution":{"observed_at":"2026-08-05T10:59:00.246080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:59:00.021769Z","title":"Hact-net: A hierarchical cell-to-tissue graph neural network for histopathological image classification","venue":null,"work_id":"7d9e015b-6d7f-4091-9815-9fdcde8e17bc","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:53.957864Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:5b51869c6a0e06a5b52565920b33bf86befbbcf6d575c9ad386337b20717805c","observation_id":"5c143cb4-4a61-4396-aeec-b8f3ab3fe18d","resolution":{"observed_at":"2026-08-05T10:59:00.105582Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:59.779831Z","title":"High timm17a expression is associated with adverse pathological and clinical outcomes in human breast cancer","venue":null,"work_id":"ea4962b8-5d9c-434f-92af-24fdb9818060","year":2012},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.131243Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:86059697406b89002c06e050750e7fd2ecf5d789d9448fddab49e927b24c9fd6","observation_id":"571f4714-a354-4610-84ff-e4c17e71b146","resolution":{"observed_at":"2026-08-05T10:58:59.882651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:59.433314Z","title":"Creb-regulated transcription co-activator family stimulates promoter ii-driven aromatase expression in preadipocytes","venue":null,"work_id":"aa53b22e-edff-4124-a5a6-981c36a0f036","year":2013},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.231200Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:843bf60a41dc16c5dd10a6cd8acb3da365c312496fbcbf747b4898fb830c9241","observation_id":"de8bed3f-ae9c-4a33-88bd-b637895e8e36","resolution":{"observed_at":"2026-08-05T10:58:59.577596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-07-06T03:53:32.549552Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-05T10:58:54.376921Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.376921Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:89bfbd32699f035683193a408520879859f9516ec936f214f03ad8b759b9498d","observation_id":"4ab90da5-2892-4920-8964-7cb49b9f15c3","resolution":{"observed_at":"2026-08-05T10:58:54.376921Z","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":"2026-08-05T10:58:59.129631Z","title":"Multimodal deep learning for biomedical data fusion: a review","venue":null,"work_id":"528d8410-444d-4584-a28f-a5790821b941","year":2022},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.525919Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:71d62ec4d318cc5074073e336793130a77a48efd051d73e4a7922b28a8de8617","observation_id":"3794f666-941b-4963-aae1-e3510a07e44f","resolution":{"observed_at":"2026-08-05T10:58:59.241124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:58.784544Z","title":"Multimodal data fusion for cancer biomarker discovery with deep learning","venue":null,"work_id":"08781576-9594-4175-bd78-b44fb37e6a65","year":2023},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.623908Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:09c49aa32a1d1eab5af07c01d939c39bb0d291c4b747feb9ebaf78f96570ff1e","observation_id":"e4cbb41c-27d0-4d7c-9db9-0dc89a556c18","resolution":{"observed_at":"2026-08-05T10:58:58.944561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:58.472239Z","title":"Axiomatic attribution for deep networks","venue":null,"work_id":"33cc53a2-08e5-4979-85cd-421a08f30198","year":2017},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.785331Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:468f1d3b3a61d2e948b471f9cde34fada215c4ea76c21e61136362207e66ca50","observation_id":"e828eede-0d35-4e84-a7f1-052cfe698b00","resolution":{"observed_at":"2026-08-05T10:58:58.585336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:58.165076Z","title":"Rethinking the inception architecture for computer vision","venue":null,"work_id":"53647e9b-c4fd-49cc-8882-3e8dd152081f","year":2016},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.894186Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:60feaa95ef23b615d59d545b248be21fd399b93c08e0b0be8352ad4fa15befa8","observation_id":"e2cf51b5-31ae-40d6-9e88-7a32d87fc0e3","resolution":{"observed_at":"2026-08-05T10:58:58.303691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:54.998127Z","title":"Fusionbench: A comprehensive benchmark of deep model fusion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:54.998127Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:bb9296f8a16738660f24b7fce0b1aeb4c08ca6dd902df5ca4ab34f6431665b4f","observation_id":"d8055292-1f85-44d4-b865-ec78bbb84f32","resolution":{"observed_at":"2026-08-05T10:58:54.998127Z","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":"2026-08-05T10:58:57.804978Z","title":"Her2 immunohistochemical scores provide prognostic information for patients with her2-type invasive breast cancer","venue":null,"work_id":"a4bebc5b-9de8-4f10-8995-d182753ecb5c","year":2019},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.117387Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:5ba9daf8acfd91ac040f377ea819a920494c881b9609c6b0230aad9431cb6baf","observation_id":"7efae94d-ed5d-46c4-a725-76cc4d932f1a","resolution":{"observed_at":"2026-08-05T10:58:57.990650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:57.519602Z","title":"Lung cancer subtype diagnosis by fusing image-genomics data and hybrid deep networks","venue":null,"work_id":"a35e53be-79a5-4234-8274-8048392f9d58","year":2021},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.210397Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:286d4970dd016c9dab2fdb6b3ae5e0312fd539353e82930795e88cf9631be4cd","observation_id":"c847c00f-b844-43f7-8d70-e8ff148f518b","resolution":{"observed_at":"2026-08-05T10:58:57.663832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:57.282830Z","title":"Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies","venue":null,"work_id":"987b5688-d017-4b81-a31b-fdf70fb82709","year":2022},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.315287Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:9d52cd603699dad58d20371f70189668d2dfd6526326428f760b36301a229a45","observation_id":"9d834887-f37b-4fe7-ab5b-f0aba441706a","resolution":{"observed_at":"2026-08-05T10:58:57.417434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:57.101672Z","title":"Heterogeneous model reuse via optimizing multiparty multiclass margin","venue":null,"work_id":"8269648a-ef3f-4d06-9a7d-2c08fdc1b9e1","year":2019},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.453037Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:0f89b2a3dfb129e9e36b8227f579143caa40f0882cebe7bb01d9b4208a628c67","observation_id":"ac67c8ad-8eae-4faa-aebf-e5e3a3180443","resolution":{"observed_at":"2026-08-05T10:58:57.174436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:56.841213Z","title":"Quantitative proteomics study of breast cancer cell lines isolated from a single patient: discovery of timm17a as a marker for breast cancer","venue":null,"work_id":"29d17c51-200e-4534-98e7-82068e689f1d","year":2010},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.562232Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:a4caff5e1e208add70683ed25a1ea5ac399a2bc662d6554b57fb2240a08bd342","observation_id":"d3ee3bc6-3cd4-4eff-a830-d1aa45af49da","resolution":{"observed_at":"2026-08-05T10:58:56.966539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:56.663024Z","title":"The impact of timm17a on aggressiveness of human breast cancer cells","venue":null,"work_id":"419c8c0e-41fd-4868-8fd6-be40007bf263","year":2016},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.699106Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:7e8e1d6e27ced83466af14a708fbb22a922823612a41ac55b8319e57fe5e8f4b","observation_id":"ec5bdef4-aac3-41c6-9c52-b9d773ce6944","resolution":{"observed_at":"2026-08-05T10:58:56.793404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:56.470413Z","title":"Triple-negative breast cancer molecular subtyping and treatment progress","venue":null,"work_id":"08311172-7176-4f30-998c-774c38f1a6df","year":2020},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.815549Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:1bd5da653cef85d131e0d0c2e482414658b4c7b70f424e745af0d832a791c42c","observation_id":"382805ef-0d1e-47a3-a933-61ef056789b8","resolution":{"observed_at":"2026-08-05T10:58:56.543468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:58:56.265991Z","title":"Analysis of tumor genomic pathway alterations using broad-panel next-generation sequencing in surgically resected lung adenocarcinoma","venue":null,"work_id":"424c8379-fc56-4e27-b577-6a548ca338d0","year":2019},"citing_paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-05T10:58:55.939101Z"},"links":{"citing_paper":"/paper/2509.03408"},"observation_digest":"sha256:91d2a3f3f73339068910634c6a215f4d49cb93ff82609bcdfcc2299d01427973","observation_id":"3449f806-6658-4256-9dfc-fa2756a17451","resolution":{"observed_at":"2026-08-05T10:58:56.340265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.03408","last_updated":"2025-09-03T15:33:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-05T10:58:49.242909Z","submitted_at":"2025-09-03T15:33:04Z","title":"Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":41},"total_outbound_references":48},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2509.03408."}