{"as_of":"2026-08-12T06:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:06ac37e9840a45bc64385617c3fd30d153a32840f7483d8ba052eb471ab541f4","coverage":[{"denominator":60,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":60,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T10:24:04.507731Z","state":"measured"},{"denominator":60,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":60,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2412.16715/citation-record","integrity":"/paper/2412.16715/integrity","json":"/paper/2412.16715/citation-record.json","paper":"/paper/2412.16715"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T10:24:05.184457Z","title":"From detection of individual metastases to classification of lymph node status at the pa- tient level: the camelyon17 challenge","venue":null,"work_id":"a6e2769a-6211-4835-b844-74007a75d522","year":2018},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.272040Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:f6b28bc8abb43b720994c2e8752421f8ce32db9f54e49e2883b38bd05a918733","observation_id":"59b14216-7ba1-472f-8c97-1c801a253226","resolution":{"observed_at":"2026-08-11T10:24:05.188714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.171448Z","title":"Artifi- cial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge","venue":null,"work_id":"67e6d4ee-e914-4861-8a06-4e6eeb8d319c","year":2022},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.276352Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:76c9a972037697813995b948ae5bc95825a0cb45bdee5500e71970e801fe0ee7","observation_id":"95a2cd3c-daf1-412d-bc96-538841853e41","resolution":{"observed_at":"2026-08-11T10:24:05.176260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.160039Z","title":"Histopathology whole slide image anal- ysis with heterogeneous graph representation learning","venue":null,"work_id":"cab05f77-274e-4fad-9fdc-8641648c5dcb","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.280112Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:a5d64e92a513e1ba01a26efdc141517f352ef6e682a6d801a21f14e0dd0adf46","observation_id":"3304cbb2-5fbd-4909-bbda-a8eead3c8778","resolution":{"observed_at":"2026-08-11T10:24:05.164001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.148477Z","title":"Whole slide images are 2d point clouds: Context-aware survival prediction using patch-based graph convolutional networks","venue":null,"work_id":"3568ae4b-447f-4337-bc1f-042ae600f416","year":2021},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.284552Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:1aa51f60e75c6e65e0fd86f6277f223a82d699cea840607c4b14be96d8e5ecbf","observation_id":"7bf0d78b-74fe-479e-b32f-8e10d78e7631","resolution":{"observed_at":"2026-08-11T10:24:05.152485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.136813Z","title":"Scaling vision transformers to gigapixel images via hierarchical self-supervised learning","venue":null,"work_id":"46fa0f29-394f-4b85-93e8-def379ca0590","year":2022},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.288718Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:b5188c84beea107c2fabb46ff174660a9aa439a2b4b8f11595489f731485d73f","observation_id":"d21bdba9-875f-46f9-a41b-7bb2e6c76110","resolution":{"observed_at":"2026-08-11T10:24:05.140577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.293841Z","title":"Towards a general-purpose foundation model for computational pathology","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.293841Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:48c53262adba58b3091386f3641b5b51c14e414052d0275966e60eaa3e614ab5","observation_id":"4e3f8be5-d0aa-415c-b535-b59c6f84824d","resolution":{"observed_at":"2026-08-11T10:24:04.293841Z","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":"2026-08-11T10:24:04.298494Z","title":"Largekernel3d: Scaling up kernels in 3d sparse cnns","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.298494Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:cecf6f81e9696f244a15d02fa827652e2332c83b8dc9d6d8647b7979b37c6627","observation_id":"76c00924-5731-427b-a088-f68170d06d9d","resolution":{"observed_at":"2026-08-11T10:24:04.298494Z","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":"2026-08-11T10:24:04.302660Z","title":"4d spatio-temporal convnets: Minkowski convolutional neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.302660Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:c39ed6f1ebcbecdf5df02041940cf9d1e58b9753297efcf8f449fb74a0a8d1b1","observation_id":"42876fcc-1b6e-43d2-9bee-78170f145633","resolution":{"observed_at":"2026-08-11T10:24:04.302660Z","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":"2026-08-11T10:24:04.306776Z","title":"Spatial architecture and arrangement of tumor-infiltrating lympho- cytes for predicting likelihood of recurrence in early-stage non–small cell lung cancer","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.306776Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:7eff42544902b14efb3cc4dfa69bf853a4b9595fa85ebea1a5c25621aa7f04a4","observation_id":"794f2d81-e73a-4879-9d36-0bf4f498a31c","resolution":{"observed_at":"2026-08-11T10:24:04.306776Z","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":"2026-08-11T10:24:05.095510Z","title":"Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner","venue":null,"work_id":"2bbb4944-52de-4a65-816b-fb27a699b9aa","year":2017},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.310774Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:3fba865fa93ea9cd2dfc40e2e2a03a561767153a11a43664058f4d9b9e561f7d","observation_id":"e1cdec0b-2261-48db-96cb-de04cc1b30ef","resolution":{"observed_at":"2026-08-11T10:24:05.100473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.083438Z","title":"Bladder cancer","venue":null,"work_id":"10a97381-07d3-4ecb-a560-27cf4e58a35b","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.314693Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:6062906e5c264899af7bd671d6ff569731aef47f830b3affdc47a249f52f8959","observation_id":"8213d74c-1a70-4ebf-9df3-29f3d75b23cb","resolution":{"observed_at":"2026-08-11T10:24:05.087579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.070888Z","title":"An outcome pre- diction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the ssign score","venue":null,"work_id":"955f0503-2f7a-405f-88cd-467034cd9ebc","year":2002},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.318685Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:76be7069020ae444cc6b37ec9679a89e53c8058a5be437d8c411d8e070347211","observation_id":"466807a8-839b-455a-8020-fb56a695332e","resolution":{"observed_at":"2026-08-11T10:24:05.075369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10778","last_updated":"2020-04-22T08:52:04Z","snapshot_observed_at":"2026-07-06T09:06:56.021993Z","submitted_at":"2020-03-24T11:25:12Z","title":"PanNuke Dataset Extension, Insights and Baselines","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10778","snapshot_observed_at":"2026-08-11T10:24:04.322489Z","title":"Pan- nuke dataset extension, insights and baselines.arXiv preprint arXiv:2003.10778, 2020","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.322489Z"},"links":{"cited_paper":"/paper/2003.10778","citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:4bd3800b4ff7e4a8c221ca3a1fd39985c31b86cf2cc071257bc209378a376b36","observation_id":"80ae0b7a-82c9-456b-8677-0c5696ac9b6a","resolution":{"observed_at":"2026-08-11T10:24:04.322489Z","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":"2026-08-11T10:24:05.058404Z","title":"3d semantic segmentation with submani- fold sparse convolutional networks","venue":null,"work_id":"d82b6535-5947-4f97-b438-8a12156e993a","year":2018},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.326934Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:8ecf3e24a981a2b787c31c2102f7bf8a9f38c3acf0b899dc48f96b6bb4ae553c","observation_id":"7589a3cc-6f57-4203-961c-6a342d1bcc26","resolution":{"observed_at":"2026-08-11T10:24:05.062440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.046459Z","title":"Hover-net: Simultaneous segmentation and classi- fication of nuclei in multi-tissue histology images","venue":null,"work_id":"8c00edfa-f418-4992-8b6e-4055cad80b82","year":2019},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.331093Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:c42bb4deaf7fa74570535049375e91addc13e8ea8766a425f64b13abb93768f3","observation_id":"6774058d-b462-40ac-942b-703f39ea541e","resolution":{"observed_at":"2026-08-11T10:24:05.050621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.034525Z","title":"Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification","venue":null,"work_id":"096016bd-d797-4f70-9d1f-f66550f48e76","year":2021},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.335079Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:0eb04a83e7d12b75f3e182037774d292441bd2df193cd57c9ce4a8bf79f1e5e4","observation_id":"aa0f6d1a-c466-4041-9cb0-322f2385bfe5","resolution":{"observed_at":"2026-08-11T10:24:05.038489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:05.023102Z","title":"Toward a shared vision for cancer genomic data","venue":null,"work_id":"cba61118-3076-456d-a790-dbf14571f07f","year":2016},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.338767Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:6e7f6c2a9b87f175c8af6150a0e76d73c06197ac6938abbfc64ae4aef2775bdf","observation_id":"c0d86e0c-7f5b-4b9c-9ed8-7637c5e79915","resolution":{"observed_at":"2026-08-11T10:24:05.026992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.342422Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.342422Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:161d5bcad961a9e26cf684dcd53217adc5c85d1fa16e8b50da947abbfbe1d670","observation_id":"dc69454a-e90b-4b90-8fca-b6ef0ae7c0bb","resolution":{"observed_at":"2026-08-11T10:24:04.342422Z","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":"2026-08-11T10:24:05.004941Z","title":"Quilt-1m: One million image-text pairs for histopathology","venue":null,"work_id":"972c8af9-f33a-4d4e-b974-d5d3ba9f47de","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.346099Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:e510d694485f8d0068d0b877690ad534524823a54ddbe85d5d8465e38d93ae13","observation_id":"5c7f7567-7a15-4923-9769-075371788033","resolution":{"observed_at":"2026-08-11T10:24:05.009138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.993971Z","title":"Attention-based deep multiple instance learning","venue":null,"work_id":"9e086e9d-3142-4a30-8a2c-d8242faf9ee7","year":2018},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.350344Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:1a0525ec0fa1fc56b2f7cd715ceb9cb805a4a1144794f053ee867a55725cfe39","observation_id":"efaf719b-f366-47c4-9ef1-65fee949a1e4","resolution":{"observed_at":"2026-08-11T10:24:04.997770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.982819Z","title":"Model- ing dense multimodal interactions between biological path- ways and histology for survival prediction","venue":null,"work_id":"50cd1654-38e6-45f1-ae97-c39bd8f85f3d","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.354180Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:9e5ff5d6507ca5e7308942a8c57f2bf44056292b726fd0cba598e306d35cb71b","observation_id":"654272c5-1582-4aaa-ba9b-c10d7429323c","resolution":{"observed_at":"2026-08-11T10:24:04.986684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T10:24:04.357926Z","title":"Adam: A method for stochastic opti- mization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.357926Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:184f012cb4be443b391ea66506a604cc6505a641d206aa40bef21f279cb86b28","observation_id":"559495d7-ea00-46a2-ac1b-0f0471d859cf","resolution":{"observed_at":"2026-08-11T10:24:04.357926Z","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":"2026-08-11T10:24:04.972287Z","title":"A dataset and a technique for generalized nuclear segmentation for computational pathology","venue":null,"work_id":"ff9972e1-cfdd-4c27-8d8d-94c7a864fe23","year":2017},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.362131Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:8896b32ac831ef6a742845109be680dc33743ff1e2947702e4493df4b369b824","observation_id":"92ae4ff0-3e60-41d0-a077-38ebe26570b6","resolution":{"observed_at":"2026-08-11T10:24:04.975811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.961727Z","title":"Pointpillars: Fast encoders for object detection from point clouds","venue":null,"work_id":"ce1ba382-a30a-43e6-9fc0-5f3ea170d42b","year":2019},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.366089Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:1480134eab24e51bb6b3ba3e5e9934b7cf2e9eaf4979dea8a24063b355e21d52","observation_id":"6dcf950e-f85a-4df8-865b-0dd10dddfa66","resolution":{"observed_at":"2026-08-11T10:24:04.965389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.951584Z","title":"Dynamic graph repre- sentation with knowledge-aware attention for histopathology whole slide image analysis","venue":null,"work_id":"710303b5-9d8f-42fa-a9ab-8d2ec5d6f99c","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.369942Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:f3028f8732e010309a357cfd7586dd995a1e4b10aef8e84ccf514b3064fc2a89","observation_id":"70b29bf1-7a8d-4364-94c8-9de8b13ef832","resolution":{"observed_at":"2026-08-11T10:24:04.954852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10739","last_updated":"2024-11-25T01:45:35Z","snapshot_observed_at":"2026-08-10T12:05:50.451265Z","submitted_at":"2024-02-16T14:56:13Z","title":"PointMamba: A Simple State Space Model for Point Cloud Analysis","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10739","snapshot_observed_at":"2026-08-11T10:24:04.373658Z","title":"Pointmamba: A simple state space model for point cloud analysis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.373658Z"},"links":{"cited_paper":"/paper/2402.10739","citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:29a6ff56fbad6a78a36d54b0ec874fe479860fac980ea4c9413523024161d4a1","observation_id":"cb07069c-b898-49b6-849c-e84c94565c44","resolution":{"observed_at":"2026-08-11T10:24:04.373658Z","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":"2026-08-11T10:24:04.940044Z","title":"Interventional bag multi-instance learning on whole-slide pathological images","venue":null,"work_id":"ad71d22e-846e-4eb3-8435-2a1719bcfad4","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.377933Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:cd31a00c30fbdd28d93eb1a183a4b34908364b6788e3058605158bb364ca03f6","observation_id":"e32e2920-56f9-4644-9786-1fe40d3b754a","resolution":{"observed_at":"2026-08-11T10:24:04.944141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.928883Z","title":"An integrated tcga pan-cancer clinical data resource to drive high-quality survival outcome analyt- ics","venue":null,"work_id":"dd53b535-9a8f-4598-9d32-f0d7cefb6f39","year":2018},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.381725Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:d01897d76363f9a6ab197a78e5e4c001def55d52ea64d52dadece77127868223","observation_id":"4b11424f-12ad-4615-b26b-389e2295a9e3","resolution":{"observed_at":"2026-08-11T10:24:04.932731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.03265","last_updated":"2021-10-26T02:48:30Z","snapshot_observed_at":"2026-08-11T14:56:04.364712Z","submitted_at":"2019-08-08T20:51:17Z","title":"On the Variance of the Adaptive Learning Rate and Beyond","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.03265","snapshot_observed_at":"2026-08-11T10:24:04.385407Z","title":"On the vari- ance of the adaptive learning rate and beyond.arXiv preprint arXiv:1908.03265, 2019","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.385407Z"},"links":{"cited_paper":"/paper/1908.03265","citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:46b87652e34272787a86000b110437cea0c780d632f0c290b90f8f5f836eb950","observation_id":"128c70fd-0c9b-4d1b-8255-ca8d832252b5","resolution":{"observed_at":"2026-08-11T10:24:04.385407Z","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":"2026-08-11T10:24:04.917414Z","title":"Data-efficient and weakly supervised computational pathology on whole- slide images","venue":null,"work_id":"050d49ea-001d-4f6e-8a9f-3b33f9108882","year":2021},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.389771Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:02fa7020c5765b240f46a93dfb792eb54c6f9109a521c73883ac8dbc5d9c3ed8","observation_id":"22e11750-f023-4ad0-a928-0d734d3599d5","resolution":{"observed_at":"2026-08-11T10:24:04.921698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07814","last_updated":"2023-12-13T00:24:37Z","snapshot_observed_at":"2026-07-06T17:00:49.692941Z","submitted_at":"2023-12-13T00:24:37Z","title":"A Foundational Multimodal Vision Language AI Assistant for Human Pathology","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.07814","snapshot_observed_at":"2026-08-11T10:24:04.393399Z","title":"A foundational mul- timodal vision language ai assistant for human pathology","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.393399Z"},"links":{"cited_paper":"/paper/2312.07814","citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:a5489d30492a5aa3bc8ee0ed21de18ee690bb6b6b4a3e2c1f6e9ab411a79f1ca","observation_id":"12466a4e-3b8e-44f9-a318-545ed586b2e1","resolution":{"observed_at":"2026-08-11T10:24:04.393399Z","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":"2026-08-11T10:24:04.904385Z","title":"A visual- language foundation model for computational pathology","venue":null,"work_id":"d445ffae-ee30-4266-ad32-7358cf55e288","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.397147Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:c17b22717944ae6bf65e6ae8f9e6ce0a3c2d17c3e8ab2f21dfcdb51cb38f5f74","observation_id":"a709ce4f-87fb-4392-9b68-b09b02d1d5be","resolution":{"observed_at":"2026-08-11T10:24:04.909360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.892087Z","title":"A visual- language foundation model for computational pathology","venue":null,"work_id":"4952b598-6724-4cb4-921d-5e44beba0f49","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.400865Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:064d4e0f41da5bf7c37b43d2836819525b5e7bafa072634a66571b40064eebae","observation_id":"36391585-47f4-4f7a-a52b-aa705cc3110d","resolution":{"observed_at":"2026-08-11T10:24:04.896073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.879499Z","title":"Re- thinking network design and local geometry in point cloud: A simple residual mlp framework","venue":null,"work_id":"63f4677c-61a0-409c-9285-c15e5e7802a4","year":2022},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.404546Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:365e4d31162efb761248e0089790a31c34c203e57297ae08c8c780dd859dde6f","observation_id":"f881144f-bb12-4b41-b455-aa23545788f7","resolution":{"observed_at":"2026-08-11T10:24:04.884419Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.868081Z","title":"V oxnet: A 3d con- volutional neural network for real-time object recognition","venue":null,"work_id":"9b421fa0-aea6-4144-a072-43a400efbf29","year":2015},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.408071Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:0e91bf7aac24ef086e95eb4df7bd78e1eb2a553f58b74834a57009a629d4c74f","observation_id":"60811809-439c-463a-aa95-eb5666af4c60","resolution":{"observed_at":"2026-08-11T10:24:04.872335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.855886Z","title":"Sparse multi-modal graph transformer with shared-context processing for representation learning of giga-pixel images","venue":null,"work_id":"f72e4428-6057-45fe-b909-48fb26376088","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.411682Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:b7df5ae7c4e959f23c66389f58b4ceb11a4f67f6e218aaab076c5a229144994b","observation_id":"21035614-5893-430a-ae8d-e5bc1473883f","resolution":{"observed_at":"2026-08-11T10:24:04.860596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.844387Z","title":null,"venue":null,"work_id":"cecc9efa-2e12-4bdd-9151-e545271dc525","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.415493Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:47d2b74c395e3c52c9a0bdf55dd45f359657bda8d3685bad8ef98483c3e30727","observation_id":"88050d34-d26a-4fb6-b087-c8c18ec9364c","resolution":{"observed_at":"2026-08-11T10:24:04.847931Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.419106Z","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.419106Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:1b6ab461ecc303f9045ac725aae2002deaddb9577455c1d7bf5c427af65c49c0","observation_id":"88bdbdb2-090a-450e-81a3-40e5d9d12c23","resolution":{"observed_at":"2026-08-11T10:24:04.419106Z","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":"2026-08-11T10:24:04.825336Z","title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","venue":null,"work_id":"66594b5b-f405-4f4a-837a-40b6c4776f27","year":2017},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.423060Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:7c981df7dbaca30a747a89dc26407ec722c4d78c3b6a758aa111c64410f628b4","observation_id":"e1080d40-eede-440d-9f9d-56fe23780232","resolution":{"observed_at":"2026-08-11T10:24:04.830078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.814513Z","title":"Ivt: An end-to-end instance-guided video transformer for 3d pose estimation","venue":null,"work_id":"1f06de9a-1011-42f0-8de4-7913e0609a20","year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.426684Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:c9b7839db38caea1e7f1d8d633798fc3a5054a95642cff29612e2d8e97f4860f","observation_id":"5445ff48-dbd6-4600-aeee-07ff4f6f6ecf","resolution":{"observed_at":"2026-08-11T10:24:04.818076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.803216Z","title":"Learning degradation-robust spatiotemporal frequency-transformer for video super- resolution","venue":null,"work_id":"b9893a30-0df8-416d-bc21-c8b8ce44df3d","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.430511Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:781ec4ccd57072eb3913f9ee55a80363000b3921d8864377b273885de7d8b03c","observation_id":"e5a4f1f4-f87c-444e-953e-15f689e4bcdd","resolution":{"observed_at":"2026-08-11T10:24:04.807203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.03804","last_updated":"2024-09-05T09:14:02Z","snapshot_observed_at":"2026-08-12T06:31:00.795350Z","submitted_at":"2024-09-05T09:14:02Z","title":"End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.03804","snapshot_observed_at":"2026-08-11T10:24:04.434084Z","title":"End- to-end multi-source visual prompt tuning for survival analy- sis in whole slide images","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.434084Z"},"links":{"cited_paper":"/paper/2409.03804","citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:8e3342ffd9f4e09364325dc65e2f6f32f91e47afda0c2218ae0d07d9467825c8","observation_id":"361a7173-0877-4002-b881-b74c3e76953f","resolution":{"observed_at":"2026-08-11T10:24:04.434084Z","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":"2026-08-11T10:24:04.791557Z","title":"The digital brain tumour atlas, an open histopathology resource","venue":null,"work_id":"8704724e-3b3e-4bf5-a59c-3608c2978613","year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.438435Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:2d03eb616d69795445533316ff157655742941235eb780cfa3b082f51cadc9cf","observation_id":"6f98e43d-3b3f-4b60-8fc1-4bd831296d8f","resolution":{"observed_at":"2026-08-11T10:24:04.795564Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.442627Z","title":"Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.442627Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:765ab2bfa48e9c89166f1548fa3afa6a5e3fdfbb4c8f0735d6423e2eb850de92","observation_id":"2b32e3de-c1ff-4a42-9bfc-2e35f4250a9a","resolution":{"observed_at":"2026-08-11T10:24:04.442627Z","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":"2026-08-11T10:24:04.772458Z","title":"The molecular and cellular heterogeneity of pancreatic ductal adenocarcinoma","venue":null,"work_id":"24e0cb2a-8118-4d22-bf3e-8215558e8669","year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.446679Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:43a9402dc7c6257f8972f0798d9d4b219c6170fc6724cc35437ef38b423085ad","observation_id":"aed35819-f0fd-44ce-91ac-4e72e80c515f","resolution":{"observed_at":"2026-08-11T10:24:04.776468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.760629Z","title":"Tumor micro-environment interactions guided graph learning for survival analysis of human can- cers from whole-slide pathological images","venue":null,"work_id":"af858be0-2498-493f-a739-584cba8cd826","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.450549Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:3672137c3678ec6aad3b132a350de285cac77038caebd72eb8751bb92dfb7cd8","observation_id":"9b02b1a6-5b70-40d8-ba05-d502a6bcd852","resolution":{"observed_at":"2026-08-11T10:24:04.764547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.749482Z","title":"Transmil: Transformer based correlated multiple instance learning for whole slide image classification","venue":null,"work_id":"a971a4f3-aec4-4f56-a78b-de8366f9b59b","year":2021},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.454636Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:c172dbcadb3f12ca84d5def69cd0187eacf39144d22b60fc82c61068356f947d","observation_id":"db173581-03d0-45c2-a320-86e43c3f597c","resolution":{"observed_at":"2026-08-11T10:24:04.753331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.737756Z","title":"Dpa-p2pnet: Deformable proposal-aware p2pnet for accurate point-based cell detection","venue":null,"work_id":"c885c9ad-a925-47cb-b585-1dcc7a24ad21","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.458926Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:8445ca5fdd0798cc7bf5aebd234a799f112617ad077bc1f7eec35a12a422389f","observation_id":"1f05458e-a016-4615-949b-25f8cc63ff95","resolution":{"observed_at":"2026-08-11T10:24:04.742220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.726433Z","title":"Mor- phological prototyping for unsupervised slide representation learning in computational pathology","venue":null,"work_id":"7a6a8e45-5304-4dce-8900-43078d414d59","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.462962Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:bd8f763e6cbeeaedb0c25d41116f348e3aadbf1003dee98e54a0b8751083823f","observation_id":"6d8bd9a6-9aed-4f48-a533-0526a3fd916d","resolution":{"observed_at":"2026-08-11T10:24:04.730426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.714203Z","title":"A postoperative prognostic nomogram predicting recurrence for patients with conven- tional clear cell renal cell carcinoma.The Journal of urology, 173(1):48–51, 2005","venue":null,"work_id":"94ac75d5-64e6-4f2c-b2cf-fd6eb1d996af","year":2005},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.466663Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:ed564b188aacbd9ca15b7a1bddcb8b537b6794d62a81b138f083d9b6985b3da4","observation_id":"96deeec0-2fd6-4e0a-8327-d86528cf01a4","resolution":{"observed_at":"2026-08-11T10:24:04.718338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.702398Z","title":"Pathasst: Redefining pathology through generative founda- tion ai assistant for pathology, 2023","venue":null,"work_id":"549ab54e-4247-4498-9a8a-863ad77e7166","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.470646Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:bf1cdd70b495b76ce30eaf9b855d449436c46e8123ee987c2bb39c5fb8a8862b","observation_id":"3505f66c-a793-4ea2-8609-d014f72974ac","resolution":{"observed_at":"2026-08-11T10:24:04.706477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.690870Z","title":"Methods for segmen- tation and classification of digital microscopy tissue images","venue":null,"work_id":"7ec93f1a-f6ab-44a6-b60a-17a371450ce2","year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.475695Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:3b72ed0bf7692744eda45120a8d4a2b7f73455026910fed022d9da880f6cdeea","observation_id":"687f545a-f117-4fff-99ce-ebff29dbdafa","resolution":{"observed_at":"2026-08-11T10:24:04.694981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.678539Z","title":"Octformer: Octree-based transformers for 3d point clouds","venue":null,"work_id":"963681af-091e-41c2-9c32-d458757c094e","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.479669Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:cb4fb004d0ffb0ccac71eda73d4675128cde05959fcb666ec2cced01497cb59c","observation_id":"0a12505d-11af-4d70-9442-d59856488a71","resolution":{"observed_at":"2026-08-11T10:24:04.682970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.666356Z","title":"Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images","venue":null,"work_id":"d054f650-819d-4c98-8cf1-267bb58a8614","year":2023},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.483323Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:b16ef01141e01eb4428d44d9a4730b300f036a82a4c67c812c3e7299ea9ebf57","observation_id":"da62e28b-6242-41b9-8b88-1c961f244ef1","resolution":{"observed_at":"2026-08-11T10:24:04.670638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.487079Z","title":"A pathology foundation model for can- cer diagnosis and prognosis prediction","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.487079Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:d856f9b6f80c78adc2780c520cdb28994a0b3fe8e8c742d4ffaabeb79a9deb74","observation_id":"d7009059-165d-4fe0-af30-fa710f708132","resolution":{"observed_at":"2026-08-11T10:24:04.487079Z","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":"2026-08-11T10:24:04.492262Z","title":"Point transformer v2: Grouped vector atten- tion and partition-based pooling","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.492262Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:d6ebdd434ddcccc69345b8d177934473fba0a1343b2b4653edfef2d8c75a50c8","observation_id":"68c0a926-117e-4c9a-a920-ca68d505fab9","resolution":{"observed_at":"2026-08-11T10:24:04.492262Z","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":"2026-08-11T10:24:04.642087Z","title":"Point transformer v3: Simpler faster stronger","venue":null,"work_id":"d226b3fe-284b-4b14-bcf2-a307e598c225","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.496507Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:e6ef81f628392e4cee5a94f3955c4027ea9193020ce07564d86474daf66266b7","observation_id":"52b4d7d0-a2d8-46b7-a430-19847f5b0e84","resolution":{"observed_at":"2026-08-11T10:24:04.645998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.630922Z","title":"A whole-slide foundation model for digital pathology from real-world data","venue":null,"work_id":"d60874ca-ac04-4485-b319-50400a495cc6","year":2024},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.500135Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:4d06e7c485f9507c8a363d43e695f5fe3689293bada67bcb435074519228a036","observation_id":"2f7dde4e-de73-41e6-ae81-e634a9c90b3d","resolution":{"observed_at":"2026-08-11T10:24:04.634801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.618364Z","title":"Pointweb: Enhancing local neighborhood features for point cloud processing","venue":null,"work_id":"e0feab94-a294-499a-a66b-8ea9eba0ebe6","year":2019},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.503639Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:dd698d9d16210997264d1115af15e70d652007faaa0ebcf637df5cdbe5a2585e","observation_id":"b168be1b-c8aa-42b6-be66-182fd30a8a34","resolution":{"observed_at":"2026-08-11T10:24:04.623268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T10:24:04.604124Z","title":"Point transformer","venue":null,"work_id":"4c79ef18-7777-4a39-85ed-db71e47b8f26","year":2021},"citing_paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T10:24:04.507731Z"},"links":{"citing_paper":"/paper/2412.16715"},"observation_digest":"sha256:62b06f922a6c15c9b8bdc5105ca857193e5d04c7c3caf5310ed8a5924f3b1932","observation_id":"6e32a759-364d-4e38-b646-18c51d340883","resolution":{"observed_at":"2026-08-11T10:24:04.610081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.16715","last_updated":"2024-12-21T17:57:12Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T14:58:49.399700Z","submitted_at":"2024-12-21T17:57:12Z","title":"From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer"},"reference_resolution":{"displayed":60,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":0,"verified_fuzzy":44},"total_outbound_references":60},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2412.16715."}