{"as_of":"2026-08-14T23:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3c91bf16eb48c9ea38df50c823b34027547577c1e8f6d9b39cf9980cf6ba9db7","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T18:35:00.782704Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2411.11458/citation-record","integrity":"/paper/2411.11458/integrity","json":"/paper/2411.11458/citation-record.json","paper":"/paper/2411.11458"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.395050Z","title":"Deep learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.395050Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:f144858a1630f2d6d7765ba32c72d4b1bada60fa25876ab0455cad3c8fb3d321","observation_id":"96d64c3e-9ed8-46d2-992d-d009bd28f550","resolution":{"observed_at":"2026-08-12T18:35:00.395050Z","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":"2026-08-12T18:35:02.074443Z","title":"Deep learning in histopathology: the path to the clinic","venue":null,"work_id":"61208378-bc6d-4469-a590-d0349e30088a","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.399874Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:5372bc252334d501e16b913195b66ba6161be103a9dcbb90c91b0b8775748318","observation_id":"b239a816-bf7f-41b2-a181-94366861b317","resolution":{"observed_at":"2026-08-12T18:35:02.080922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:02.041198Z","title":"Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology","venue":null,"work_id":"3db3f332-a56f-4c79-9e90-213460f6c7d8","year":2019},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.411450Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:a64984ddbe76b1eae2a8d4a7c7e0e0d5c64af8690943fc28203ba9af30f5186e","observation_id":"a01abe41-1fde-49e3-8404-0b5d8ab5f0da","resolution":{"observed_at":"2026-08-12T18:35:02.046293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:02.026210Z","title":"Simko, Sandy DeVries, Emmalyn Chen, Edward M","venue":null,"work_id":"245ce71f-2fbb-4977-b325-094628f36d8a","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.415751Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:ac6990014973877ee2a45fd9825f2cff8380a2e703e6728c50d4dc988694b332","observation_id":"12747168-442c-4f68-987c-d6b98220a600","resolution":{"observed_at":"2026-08-12T18:35:02.031011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:02.009354Z","title":"Spratt, Siyi Tang, Yilun Sun, and et al","venue":null,"work_id":"47404bb1-74cb-4ebc-a72b-fa8c0f7db9c9","year":2023},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.420748Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:3a9cebd2e371d0ea08a31fda8b9c0b23bd491dc5a19725bd51ddac04b35f10f6","observation_id":"65d0746f-3ab1-4556-b579-7b59ea4d6ae8","resolution":{"observed_at":"2026-08-12T18:35:02.015133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.986241Z","title":"De- velopment and validation of a deep learning algorithm for improving gleason scoring of prostate cancer","venue":null,"work_id":"059570d0-5c12-4570-9900-9c590dcc8bb4","year":2019},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.427448Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:e5f77758d201ee53480ae7fdcd53682921f86482742fa678c33f28f6b35e24b9","observation_id":"19848c43-0cab-4e79-87bc-c8f8acf52053","resolution":{"observed_at":"2026-08-12T18:35:01.993663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.965221Z","title":"Stain-transforming cycle- consistent generative adversarial networks for improved segmentation of renal histopathology","venue":null,"work_id":"26fee888-71b0-42ac-a499-07cafd610815","year":2018},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.434027Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:bb32dfd63dcb73435316f672b5ad483a0f037bf222e18c8873942ffb82c3f47d","observation_id":"c483068e-e4ee-4e26-84c2-10b4bd43e380","resolution":{"observed_at":"2026-08-12T18:35:01.971847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.933995Z","title":"Ar- tificial intelligence-based breast cancer nodal metastasis detection: Insights into the black box for pathologists","venue":null,"work_id":"c96803a9-df2f-467f-a850-9cc6c4e0f283","year":2019},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.438277Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:429c085889b931e316b9e078891b05aa1c9de19dd640acbef5d64961f0f6ef21","observation_id":"843c5ee2-7410-4db0-8808-dd5efc7ce3ad","resolution":{"observed_at":"2026-08-12T18:35:01.946399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.913093Z","title":"To- wards machine learned quality control: A benchmark for sharpness quantification in digital pathology","venue":null,"work_id":"3f3f3e70-50e8-4f64-ae62-8a67feedabbb","year":2018},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.442208Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:3eaa7dfa1d8266622e4b8c906bf3780fed7c91877d42cc6990e4bdc0207a5554","observation_id":"e5a1d28c-f420-43a4-b765-421797695dca","resolution":{"observed_at":"2026-08-12T18:35:01.919036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.895168Z","title":"Association between surgical skin markings in dermoscopic images and diagnostic perfor- mance of a deep learning convolutional neural network for melanoma recognition","venue":null,"work_id":"8bfc87d6-ff2c-4e2a-a33b-7093f88732a6","year":2019},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.447564Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:20ba60f91a922ff5bf9fc9c4ca64b283b1f4b1eea7f94166ba4d486c74691c4a","observation_id":"6923b205-83b5-46b8-96b1-09a4c8678896","resolution":{"observed_at":"2026-08-12T18:35:01.900898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.876656Z","title":"From development to deployment: dataset shift, causality, and shift-stable models in health ai","venue":null,"work_id":"90ebb775-cdda-499d-b2d2-707b6cffc7da","year":2020},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.452218Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:e4f57cb121bb390be295a7db2d524f52388123770732c914f06d2e97a5471003","observation_id":"4ab667b2-b946-4f78-a8f8-75b2d07a024e","resolution":{"observed_at":"2026-08-12T18:35:01.881728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.15274","last_updated":"2022-12-02T12:51:20Z","snapshot_observed_at":"2026-08-13T15:13:26.134751Z","submitted_at":"2022-06-30T13:25:34Z","title":"Augment like there's no tomorrow: Consistently performing neural networks for medical imaging","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.15274","snapshot_observed_at":"2026-08-12T18:35:00.457714Z","title":"Augment like there’s no tomorrow: Consistently performing neural networks for medical imaging","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.457714Z"},"links":{"cited_paper":"/paper/2206.15274","citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:ca0f7a9451beccb00080706888e0ebafa8c04fe7fd928b495785c75368e495a0","observation_id":"06df53c9-d3ea-48d1-9cac-7d8d13b0d1a6","resolution":{"observed_at":"2026-08-12T18:35:00.457714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.464547Z","title":"Using pre-training can improve model robustness and uncertainty","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.464547Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:9c02804ab758cbfcd7a500282fb0ccdc8c3590bb7155233be4e71b75aced3b14","observation_id":"3f96e1ce-55dd-46b7-b3cd-104e272303b0","resolution":{"observed_at":"2026-08-12T18:35:00.464547Z","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":"2026-08-12T18:35:01.846963Z","title":"Srinidhi, Ozan Ciga, and Anne L","venue":null,"work_id":"f8679349-0a15-433d-957f-4efcd70c0ece","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.469370Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:be9ba888c82fbe99f495142e5c610416aa3f1501b30b35cf6254822a4b76840a","observation_id":"f29ea4ce-616b-4725-bda4-1e25c8f8651e","resolution":{"observed_at":"2026-08-12T18:35:01.852387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.831796Z","title":"High-accuracy prostate cancer pathol- ogy using deep learning","venue":null,"work_id":"8a6488b4-732d-4649-a011-8aeafdc994d4","year":2020},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.473292Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:32472134ae2a67ac2e0e9e3c3393e22f10b0c732e9b8002e77267933748ed9c5","observation_id":"b71e398b-4134-4670-a970-7067ebc0ef60","resolution":{"observed_at":"2026-08-12T18:35:01.836725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.814071Z","title":"Triage-driven diagnosis of barrett’s esoph- agus for early detection of esophageal adenocarcinoma using deep learning","venue":null,"work_id":"28f17cb2-6f59-4baf-a202-335bbcccc813","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.477893Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:f4b1b8526e3a5dbe3017c010017c0814d331038f199c3b9e6cd41a7515319731","observation_id":"0e29de12-4124-498d-8333-21533e5b622e","resolution":{"observed_at":"2026-08-12T18:35:01.820281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.797412Z","title":"Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach","venue":null,"work_id":"ab4f2756-d379-4c96-89a7-282d1babfed3","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.482332Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:3853935c964c2c4e995c88b96bf8892e71132db2e0990efcf3699610837c8380","observation_id":"314e4a64-3f7c-45df-90c6-9d5dbd83470f","resolution":{"observed_at":"2026-08-12T18:35:01.803623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.780462Z","title":"Ai-based pathology predicts origins for cancers of un- known primary","venue":null,"work_id":"b3548fcb-f5b0-4af3-8b5e-3f6aa9bd8920","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.488424Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:7dd2671166ceb7fc311a1ec5fd7bda958575937203c4a97bbcf921b24376c4a6","observation_id":"176bd29b-d9c7-4d2c-a061-4a11367c0c8c","resolution":{"observed_at":"2026-08-12T18:35:01.786643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.760192Z","title":"Federated learning for predict- ing histological response to neoadjuvant chemotherapy in triple-negative breast cancer","venue":null,"work_id":"c693fd80-9ccf-42e1-8cae-c072d4aff677","year":2023},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.494661Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:241f7688a61e148a0ce3912a8dc6f6485387a41c8318ab1ce697f06c6df34cd9","observation_id":"a800d825-8e60-4223-8733-52e313b2bc87","resolution":{"observed_at":"2026-08-12T18:35:01.767127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.744403Z","title":"An annotation-free whole-slide training approach to pathological classifica- tion of lung cancer types using deep learning","venue":null,"work_id":"1910b164-9d58-49b5-be3f-1fa89dc98809","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.498844Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:d888d3f4b032d243564b5432caf16b7be0228182a7fe115c79574c12ada62b26","observation_id":"cef5f724-67b3-4ab9-973a-654fa6bfee44","resolution":{"observed_at":"2026-08-12T18:35:01.749946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.729066Z","title":"Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning","venue":null,"work_id":"784fd714-8979-42bb-abc6-94ee75f7466f","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.503379Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:d55d1e72e131718327c66740d5b87e8ae82ed0a9e71d09127648af34b3f84894","observation_id":"274a72de-8fb4-4141-9c30-3ccbe67180fd","resolution":{"observed_at":"2026-08-12T18:35:01.734101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.714551Z","title":"Artificial intelligence in histopathology: enhancing cancer research and clinical oncology","venue":null,"work_id":"53b21572-f1bf-4d16-8365-b891f90d71ed","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.509589Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:1f8d7fdff916bd4c6bf484cd7e2a9bcb5943158b9c1149bb83a567c49908eac6","observation_id":"218748ab-74a9-4364-b962-a21b50d22b54","resolution":{"observed_at":"2026-08-12T18:35:01.719554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.685828Z","title":"Deep learning- enabled assessment of cardiac allograft rejection from en- domyocardial biopsies","venue":null,"work_id":"8ad134c3-0cd7-4a7a-a7e4-a5dd823b814f","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.514585Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:865f6910db004963df6946ac7b4b8380f93dd62265c808a41327358be072271a","observation_id":"a185c382-ed18-463f-b7d0-5caae6be1337","resolution":{"observed_at":"2026-08-12T18:35:01.701794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.670809Z","title":"Fast and scalable search of whole-slide images via self-supervised deep learning","venue":null,"work_id":"64dd03e4-d163-4059-8b12-072fbdfaa386","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.520881Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:e47b1f2bc84eac90cf9d625e2f1cb85bc70acb7c928521111e178b44a1f01bcb","observation_id":"b026b99e-6172-4488-be08-31f0a8153df1","resolution":{"observed_at":"2026-08-12T18:35:01.675503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.656386Z","title":"Data-efficient and weakly supervised computational pathol- ogy on whole-slide images","venue":null,"work_id":"95772ff2-8475-4a5d-bd1e-55ef616d7dff","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.525734Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:ab05b4c8b755dae491f3c3a43eb1b7d47c70bc4de77dd58f28053579d6bb7fc3","observation_id":"8b89daa7-b6a6-4e8a-8e6f-a1622c6448a7","resolution":{"observed_at":"2026-08-12T18:35:01.661841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.631536Z","title":"Transfusion: Understanding transfer learning for medical imaging","venue":null,"work_id":"c520c59e-1842-4971-aac1-783d5a8839c2","year":2019},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.532270Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:6d1b1fc3b58ff891df81c42127870ee733027a23b7fc8a300aa020f410d2f86a","observation_id":"7e7c8278-ce01-4c20-8864-6a28f5ce9530","resolution":{"observed_at":"2026-08-12T18:35:01.645407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.612573Z","title":"Masked autoencoders are scalable vision learners, 2021","venue":null,"work_id":"2bb30178-1648-4d33-8939-1fd50ec3f646","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.537260Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:36dd123ac7c848fe02272b02c51d272af5573778189b270a5a9ef2f98f25691d","observation_id":"7723cae2-50b7-4fa9-aac0-1b562f411d05","resolution":{"observed_at":"2026-08-12T18:35:01.617172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.593752Z","title":"Simmim: A simple framework for masked image modeling, 2022","venue":null,"work_id":"674aa69f-1cc9-40be-8965-986e6ed93d17","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.541959Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:e470f347335e17df7222196bc2e9f62d9cceb0068fe0b1a280d8523b2274776b","observation_id":"f884661b-88cf-4cd7-bbe6-dac50ea0140b","resolution":{"observed_at":"2026-08-12T18:35:01.598864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.574721Z","title":"ibot: Image bert pre- training with online tokenizer, 2022","venue":null,"work_id":"fd126b7e-3932-4ddc-8497-7cb3431644e7","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.547507Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:671ebda77ba87b81f71335435124e614083547627fababda7216e4fa35706c3d","observation_id":"b0d896cc-80c2-4ca2-807d-d3d8078d7dd1","resolution":{"observed_at":"2026-08-12T18:35:01.579767Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.556009Z","title":"Emerging properties in self-supervised vision transformers, 2021","venue":null,"work_id":"b18cb5d2-b283-4239-8a04-18c46fbd17cb","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.552129Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:28158d35a9f5a95699d8c01df244fa773bc7737a3ea017edce08b51d17c38db8","observation_id":"be5267e2-f32c-4c0d-9809-473d76c1e420","resolution":{"observed_at":"2026-08-12T18:35:01.562614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.528530Z","title":"Dinov2: Learning robust visual features without supervi- sion, 2023","venue":null,"work_id":"e882454d-5472-4adf-a50b-1db1a68bd536","year":2023},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.557407Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:fc1afd3fd69c6adf40523dd200ae7efc6097f27c294aca475a1ab2e4a0888e8a","observation_id":"a69857dd-31ef-45c8-bf16-843f5970ee5b","resolution":{"observed_at":"2026-08-12T18:35:01.537257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.512574Z","title":"Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S","venue":null,"work_id":"206fd832-3005-476d-be97-745f9fc50130","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.562412Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:e124b80a0439817c42d25c24edc0991989114e3650374e78c540de4c50077f21","observation_id":"531d25be-0eb0-4723-b0fd-9aad8aed85db","resolution":{"observed_at":"2026-08-12T18:35:01.517265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.496805Z","title":"Unsupervised pre-training of image features on non-curated data, 2019","venue":null,"work_id":"a24e7957-7470-4903-a1d3-c6a67bcb9567","year":2019},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.569003Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:a2f5f73d08ecbb80416835b917a1795f68c6fef9ddbb29ea8341849e4e3207b5","observation_id":"94a6817f-50e8-4752-b601-c0b77e66f6c5","resolution":{"observed_at":"2026-08-12T18:35:01.501768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.481313Z","title":"Self-supervised pretraining of visual features in the wild, 2021","venue":null,"work_id":"1d4c29eb-7343-4da8-b95f-74806c8d5a17","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.573453Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:33e63f6afc7f3a19511bbdf1edbe4fa9be8ffd362fdb49bd4d0f80945fa4e8b1","observation_id":"d31b21ac-1e83-4b5f-b66b-111f24f4eb5d","resolution":{"observed_at":"2026-08-12T18:35:01.486443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.464068Z","title":"Vision models are more robust and fair when pretrained on uncurated images without supervision, 2022","venue":null,"work_id":"901399b0-79d3-4e21-a724-0a84ae1b3502","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.584080Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:3ffd1f31a717284268d1a00a856729a096f97c01462ed24df58582ffbc3d71c4","observation_id":"a44266c6-ed00-4d07-9a49-c0d8dd760581","resolution":{"observed_at":"2026-08-12T18:35:01.469377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.446437Z","title":"Sslp: Spatial guided self-supervised learning on pathological images","venue":null,"work_id":"f2addfcb-4917-48b9-b58c-81e05203b8b5","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.589956Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:c5837d719ae67b4d0634b143e34849dcc2f333e4810df7b5b6960578180d4174","observation_id":"464c030a-ca2a-4fa5-90d7-b2953c4b422e","resolution":{"observed_at":"2026-08-12T18:35:01.453862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.427837Z","title":"Improving self- supervised learning with hardness-aware dynamic curricu- lum learning: An application to digital pathology","venue":null,"work_id":"411aa825-e947-4854-be23-86c0ec96b9e5","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.593879Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:4aff32e794886b9c0207c0d62e8e0f0aacf68011b4404411086fdafd3954443e","observation_id":"77f16467-c3f4-4ca9-b125-8e11331ddd1a","resolution":{"observed_at":"2026-08-12T18:35:01.432951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.410962Z","title":"Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge","venue":null,"work_id":"b74f71d0-ff7e-4ce3-b345-d5d1c89135f6","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.598322Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:6b76c7b3fad24a43bcbc70fe4e3f5dc55fd157931d2e92790da760dd7e743336","observation_id":"34905258-3b37-4470-ad01-bc8a9f8eae43","resolution":{"observed_at":"2026-08-12T18:35:01.416424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.391740Z","title":"Peso: Prostate epithelium segmentation Preprint – HistoEncoder: a digital pathology foundation model for prostate cancer 10 on h&e-stained prostatectomy whole slide images, 2018","venue":null,"work_id":"6f86c041-75f0-4209-81eb-a109eb29bcd1","year":2018},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.608276Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:be274bbc22abb7322569eab1d6cfab29766ce4a24695289656e4ac1c85f4af48","observation_id":"1c48578b-691b-4695-8ee6-d96e29f0fe42","resolution":{"observed_at":"2026-08-12T18:35:01.398092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.370271Z","title":"The 2014 international society of urological pathology (isup) consen- sus conference on gleason grading of prostatic carcinoma","venue":null,"work_id":"ee548d4e-e4de-4a74-b6e3-b1d35c929d19","year":2014},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.612802Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:29b05b1c72de498b415b2e2bc229bdb17a277670fc34b15ed8624d7e5d5b08a3","observation_id":"2586c7fa-238b-4009-950c-cfa31e83bac0","resolution":{"observed_at":"2026-08-12T18:35:01.376952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.351770Z","title":"Xcit: Cross-covariance image transformers, 2021","venue":null,"work_id":"3b3a37b2-9e8b-4aa2-a4e5-3a86be72523e","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.618514Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:9d78a0b325cea7f859f2708464a3d45da0edb0a1c2ab883059964027f9cdc3ae","observation_id":"bafc09fe-d263-44ed-8bb3-8110c6233f96","resolution":{"observed_at":"2026-08-12T18:35:01.358056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.625682Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.625682Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:437d21ee6c3286c61a00aa2581ae0089fa17d218c923a1f8d2515c0095744ed8","observation_id":"a032f3e1-c978-4160-a2e7-1c97d16fde0b","resolution":{"observed_at":"2026-08-12T18:35:00.625682Z","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":"2026-08-12T18:35:01.323156Z","title":"Histoprep: Prepro- cessing large medical images for machine learning made easy! https: //github.com/jopo666/HistoPrep, 2022","venue":null,"work_id":"d30650a3-b872-45ad-98d1-e61395adbb11","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.630213Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:e026a9b690782477b5ac667f481d78415b0446a5b6bb265751d03154e384b6d0","observation_id":"fcd3353a-859b-411f-a306-b359c8e14648","resolution":{"observed_at":"2026-08-12T18:35:01.329867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.281342Z","title":"The capra-s score: a straightforward tool for improved prediction of outcomes after radical prostatectomy.Cancer, 117(22):5039–5046, 2011","venue":null,"work_id":"b03338f3-5d53-4e82-95f9-1eda8b03c5ba","year":2011},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.641527Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:0a8872e642cc9d50b45a804c08b60bbc216b292a830a8708c6fae450827e0bee","observation_id":"946d4a36-c190-4401-b03d-899ce8a645af","resolution":{"observed_at":"2026-08-12T18:35:01.291877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.263875Z","title":"https://www.mskcc.org/nomograms/prostate/ post_op/coefficients","venue":null,"work_id":"869e5dd5-a7c0-4e17-a228-e67a1833a52c","year":2023},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.646187Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:12ff593dabea624bac8f8aa2ffd165b55e5fd3b94ece46bb7dca79e7eb6c8da0","observation_id":"03e1ddfb-1bba-4741-8f0b-bd9259301e6d","resolution":{"observed_at":"2026-08-12T18:35:01.269707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.240162Z","title":"Nonparametric es- timation from incomplete observations","venue":null,"work_id":"e60033e4-171c-4f9e-8d9e-fb5305912624","year":1958},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.652533Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:f6e13b671d25d96cef71f55a1a5f76d5cca19b7971331d863a8b740d00adb961","observation_id":"177c1eee-3792-4cc3-abb3-e3dfb1c636f7","resolution":{"observed_at":"2026-08-12T18:35:01.246379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.659171Z","title":"Randaugment: Practical automated data augmen- tation with a reduced search space","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.659171Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:e6e9f920d9c36aecb96f428b98eb19afefc8e856cfcf6af4d1f8a83fd1f5641c","observation_id":"b80a3cf2-4ef9-429a-8827-8e901939d9b2","resolution":{"observed_at":"2026-08-12T18:35:00.659171Z","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":"2026-08-12T18:35:01.208231Z","title":null,"venue":null,"work_id":"a445a23f-e9a4-41fb-8f24-dabefe58090f","year":2018},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.664454Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:74af135fa754500c009b07ac9790475139e880dbb8c30427509439266d0abcad","observation_id":"f8217d82-95dd-4d45-9898-067473b54567","resolution":{"observed_at":"2026-08-12T18:35:01.214374Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.190893Z","title":"Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests","venue":null,"work_id":"3fa22484-2683-48e7-b07b-377f4d800184","year":2016},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.669712Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:607873ef6b974fb1557d4a710071b5be52cdd0944223820b38fec6632111244c","observation_id":"a09608dc-b097-4596-8592-a9a43df41f54","resolution":{"observed_at":"2026-08-12T18:35:01.196805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.170720Z","title":"Zhuang, F","venue":null,"work_id":"88eb6e31-70ce-4fb9-8ee5-ce67a0b72a2f","year":2024},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.674922Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:d0f221e3bae1ac00ce52235536fd6a1ffc0d211fd4b91f860b82ccbc459b6250","observation_id":"d6b15cee-4701-4008-ac56-045019380652","resolution":{"observed_at":"2026-08-12T18:35:01.175173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.148735Z","title":"Truhn, J","venue":null,"work_id":"f0cc89fe-4eba-4aca-861e-c166b8ea5a92","year":2024},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.680952Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:2e738d2a7b9da7bc1add837caf9aa25ef7f18728ceac76b9b2a5c96ab8b05b03","observation_id":"838eed57-33ef-4d6c-8c07-1d27e3e1d044","resolution":{"observed_at":"2026-08-12T18:35:01.157564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:01.125858Z","title":null,"venue":null,"work_id":"f2b0ac57-0d6b-4be6-90a3-11f8033329e0","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.686057Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:2ceb0a5a63eecda79f9432fe3974d1580683576bb5b607c8140de20a3e96388b","observation_id":"ba04499b-173d-4523-a56f-a4c17c486ef2","resolution":{"observed_at":"2026-08-12T18:35:01.131847Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10729","last_updated":"2025-01-16T16:04:07Z","snapshot_observed_at":"2026-08-14T00:00:52.355346Z","submitted_at":"2024-06-15T20:04:06Z","title":"A Comprehensive Survey of Foundation Models in Medicine","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10729","snapshot_observed_at":"2026-08-12T18:35:00.695961Z","title":"A comprehensive sur- vey of foundation models in medicine","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.695961Z"},"links":{"cited_paper":"/paper/2406.10729","citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:072c6561362738a1ed435aa7bb4bb2ee5f68631e94fae5a191a8a73fb841e1ba","observation_id":"12568aaf-7ef9-40de-a368-8a3800b7f551","resolution":{"observed_at":"2026-08-12T18:35:00.695961Z","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":"2026-08-12T18:35:01.090727Z","title":"Transfer learning for medical image classification: a literature review","venue":null,"work_id":"88cbe366-1114-42a5-8280-13406eccdef1","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.701702Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:35dbdba356bced81b2dbf42509811ec73b95af3e73ffb0155f5113297f593e6c","observation_id":"94468adb-f613-4bf4-be3e-f0477cb829f6","resolution":{"observed_at":"2026-08-12T18:35:01.101080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.709353Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.709353Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:41eb2d183d3a78ab0d58349f891223ba20c18b2416181c7946dcce33ab9c0747","observation_id":"4afa18bf-1576-4259-b2f8-2695ef22ae2f","resolution":{"observed_at":"2026-08-12T18:35:00.709353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.720107Z","title":"Going deeper with convolutions","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.720107Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:5239532f7939cf77b6fe688c494d1cf9d249422422b1c8c7138730535e9775dc","observation_id":"7dc2ac9a-59d3-4218-90fc-a224d2d0bc5d","resolution":{"observed_at":"2026-08-12T18:35:00.720107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.724445Z","title":"Swin transformer: Hierarchical vision transformer using shifted windows","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.724445Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:281e7e315246af3b0bdc5a833e31622e3d3cbb42eb4b3bfd8143bd6088f2b8ef","observation_id":"b5106622-40f0-4df3-86c5-b4053b7531ce","resolution":{"observed_at":"2026-08-12T18:35:00.724445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.729409Z","title":"A foundation model for clinical-grade computational pathology and rare cancers detection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.729409Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:44daad5f4180924a90f337cadd1a9e00f17dfcae298e46433107d7051f00a353","observation_id":"c9a11635-900d-4c39-99b9-22d110408be9","resolution":{"observed_at":"2026-08-12T18:35:00.729409Z","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":"2026-08-12T18:35:01.015823Z","title":"A pathology foundation model for cancer diagnosis and prognosis prediction","venue":null,"work_id":"0c927cf3-85b9-4cd3-a477-9fdd1d534861","year":2024},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.734970Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:32d9ce569b85e9b3fe8669d5e8a091dd4794e15430091fd0a7283b775676d980","observation_id":"10b69b7e-77ee-41c4-a1c0-69ba65c04b3c","resolution":{"observed_at":"2026-08-12T18:35:01.021453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.987188Z","title":"Predicting bio- chemical recurrence of prostate cancer with artificial intel- ligence","venue":null,"work_id":"a3af7ef6-f182-4225-8a2e-5571e975daea","year":2022},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.740347Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:a5aef453d9663b937fd83694c8a52d0a480bf8b4d33b4291ec5b771c5747887b","observation_id":"dcba5aeb-b6b0-4247-b1f1-c2b06e850421","resolution":{"observed_at":"2026-08-12T18:35:00.996350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:02.056941Z","title":"Deep learning in cancer pathology: a new generation of clinical biomarkers","venue":null,"work_id":"21d094a9-72b8-4914-be8d-4899949593c4","year":2021},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.744735Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:104a2dee3a101c29cba5a181366a3fb5a13ec0f37d0bb7e60d8dbf62370e14cc","observation_id":"4415a21a-ad92-445f-afb5-0a453f34c0fb","resolution":{"observed_at":"2026-08-12T18:35:02.062069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.968172Z","title":"Computer extracted features from initial h&e tissue biop- sies predict disease progression for prostate cancer patients on active surveillance","venue":null,"work_id":"c2383282-9ff7-4f62-be3f-4ea96b83c4e4","year":2020},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.750246Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:df8921246c8c75ddeb6ca210e9dcfbd84b764c8b8296b6aa900807f941332b52","observation_id":"e3760ca2-f71d-421d-8883-410d2c9c7d0f","resolution":{"observed_at":"2026-08-12T18:35:00.973788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.951082Z","title":"A systematic analysis of deep learning in genomics and histopathology for precision oncology","venue":null,"work_id":"d0d2a701-c613-40de-b1c4-efb89680bc48","year":2024},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.755227Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:39651a2b0dc659a7352d8dabdd9ac7d7b985b1f32b04b5cb618f97b046ca28a8","observation_id":"4bb279f7-aff0-40fb-9033-eb899f9bc816","resolution":{"observed_at":"2026-08-12T18:35:00.956636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.760752Z","title":"Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.760752Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:ac1739b9290896f7611574e2254412c4b4bb4c950cbb27a645772f82718b954a","observation_id":"8331735d-9e8b-4f4d-9ba1-4d453dc93b22","resolution":{"observed_at":"2026-08-12T18:35:00.760752Z","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":"2026-08-12T18:35:00.933259Z","title":"A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics","venue":null,"work_id":"3b77260f-e8ff-4aca-8077-66b6ef9eb140","year":2023},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.765015Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:b2edcce3b0b284269bcbaf2020979fe992c438d433945cd6bcd49ba83842719b","observation_id":"0c937f85-5e79-4ba0-a874-309dd047848b","resolution":{"observed_at":"2026-08-12T18:35:00.938811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.916323Z","title":"Artificial intelligence for digital and com- putational pathology","venue":null,"work_id":"50dc0dcc-7bf5-40dd-9440-56eb66315b38","year":2023},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.772582Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:6bf36d4d8dd635e1f5cb6a5c18f5210e17f6726c1f06c2030602e9187304f3fb","observation_id":"0077aef7-fd9d-43c8-a684-4cb81fd55971","resolution":{"observed_at":"2026-08-12T18:35:00.921687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.778260Z","title":"Foundation models for generalist medical artificial intelligence","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.778260Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:53c9409b1b190cd21fa86d7e6a6de62f68560bdee2805f9b6311061e13d2084c","observation_id":"78a3ce9e-7982-44f5-9be1-f6f80ec28fcb","resolution":{"observed_at":"2026-08-12T18:35:00.778260Z","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":"2026-08-12T18:35:00.878637Z","title":"Towards a general-purpose foundation model for computational pathology","venue":null,"work_id":"7a208e4d-c651-40e3-9e67-303df378a343","year":2024},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.782704Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:5d7068072ce7ba277c67bb34da6fdbdc68eea3529275de2b3e1c75cc95d1ec82","observation_id":"d4ff325f-a829-4917-82b0-a1802f40304b","resolution":{"observed_at":"2026-08-12T18:35:00.887519Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T18:35:00.691196Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T18:35:00.691196Z"},"links":{"citing_paper":"/paper/2411.11458"},"observation_digest":"sha256:d5d41f8776006332dbbba4675cad82fff4a1fead8327bd9b709c5c45ae1c51c6","observation_id":"073c9dc2-3834-47ab-beb9-983e2d97bea5","resolution":{"observed_at":"2026-08-12T18:35:00.691196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.11458","last_updated":"2024-11-22T13:32:01Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-13T19:26:01.621406Z","submitted_at":"2024-11-18T10:46:05Z","title":"HistoEncoder: a digital pathology foundation model for prostate cancer"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":53},"total_outbound_references":69},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2411.11458."}