{"as_of":"2026-08-21T13:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:688d16f240c782de0bd6f85937cf9133014af62b2dc85bc23948d56d0c67956a","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T17:16:16.294281Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.11034/citation-record","integrity":"/paper/2509.11034/integrity","json":"/paper/2509.11034/citation-record.json","paper":"/paper/2509.11034"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T17:16:16.045314Z","title":"Data-efficient and weakly supervised computational pathology on whole-slide images,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.045314Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:ce284a7bd2137873eb269a4624e89476b58cfa3c289ce62226dd8f8f7eeb2466","observation_id":"c1f96145-db46-4e98-ace9-f1a1ae4a8b3c","resolution":{"observed_at":"2026-08-04T17:16:16.045314Z","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":"2026-08-04T17:16:16.050958Z","title":"Multiple instance learning for digital pathology: A review of the state-of-the-art, limitations & future potential,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.050958Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:304f5759671cd035f3fa4b448772e9164b3b89994cebaa4a02171328c6e1f176","observation_id":"ad63ec41-87e5-42da-9807-736cc6122d5a","resolution":{"observed_at":"2026-08-04T17:16:16.050958Z","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":"2026-08-04T17:16:16.056290Z","title":"Pseudo-bag mixup augmentation for multiple instance learning-based whole slide image classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.056290Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:21c614a45fd0e9cb9587deb234c83f6d7c4e0c5f222b6ea833bdba81e58379ba","observation_id":"1610dfee-6c9a-4d92-8335-04ccdf519f60","resolution":{"observed_at":"2026-08-04T17:16:16.056290Z","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":"2026-08-04T17:16:16.062197Z","title":"Advmil: Adversarial multiple instance learning for the survival analysis on whole-slide images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.062197Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:b537e50226ac52b5269ac45ecfdba56789bd58665f30295795e48db43bbd1a72","observation_id":"305e3667-adb8-4a0b-996d-22035210437f","resolution":{"observed_at":"2026-08-04T17:16:16.062197Z","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":"2026-08-04T17:16:16.067425Z","title":"A framework for multiple-instance learning,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.067425Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:9ec378b74ca99572eec8f3374060f073f833904287bd44ddcca024d0c2ca3c66","observation_id":"2651a770-8f29-4285-a2d4-9dbfda1e7e1e","resolution":{"observed_at":"2026-08-04T17:16:16.067425Z","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":"2026-08-04T17:16:16.072322Z","title":"Support vector ma- chines for multiple-instance learning,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.072322Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:08b5baf9cf3cd70c06d52ce07da94da52e05d0473669ac6dd637d84f72c38c45","observation_id":"628e9277-0d09-41ff-aa11-0599affdf080","resolution":{"observed_at":"2026-08-04T17:16:16.072322Z","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":"2026-08-04T17:16:16.077740Z","title":"Multiple instance learning convolutional neural networks for object recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.077740Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:17e24d3a67f8fc780eb66a2d5de989c7c3ae430c10ccf66889aea784001c60e7","observation_id":"93b1918f-1f12-490c-a59c-c6cabd88b230","resolution":{"observed_at":"2026-08-04T17:16:16.077740Z","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":"2026-08-04T17:16:16.082083Z","title":"Attention-based deep multiple instance learning,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.082083Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:001c24ce16f896cf63a723e73fb9882f80a5383c924cedcd33adaa69e394af81","observation_id":"19b5670d-23ae-464c-9c52-71365d28800a","resolution":{"observed_at":"2026-08-04T17:16:16.082083Z","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":"2026-08-04T17:16:16.086322Z","title":"Loss-based attention for deep multiple instance learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.086322Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:675291f463726c23cc3dd8edaddd92e020d419c3d73017c3015779d449aa76e2","observation_id":"b426aabe-0ea4-44ed-8580-6ec86bdeb15e","resolution":{"observed_at":"2026-08-04T17:16:16.086322Z","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":"2026-08-04T17:16:16.090713Z","title":"Multi- instance partial-label learning with margin adjustment,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.090713Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:76bed4d317e046240cd6c2910f081b79a3f0462c9aadab182cae4a7c407b2710","observation_id":"ec4a264d-ccdf-4477-8241-6cd3924be44d","resolution":{"observed_at":"2026-08-04T17:16:16.090713Z","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":"2026-08-04T17:16:16.095291Z","title":"Miles: Multiple-instance learning via embedded instance selection,","venue":null,"work_id":null,"year":1931},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.095291Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:5aac13c4eb8e4fd424b53adc21a909822ceb80a91c4edb728be11fe0ee68efe1","observation_id":"e1cd29a5-98e0-4c6a-8b88-2f7a18623aae","resolution":{"observed_at":"2026-08-04T17:16:16.095291Z","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":"2026-08-04T17:16:16.100004Z","title":"Adaptis: Adaptive instance selection network,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.100004Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:3744493235c172c11a1cad8b3010ef20a95a2dfd25f2f692f05b4809a23681d2","observation_id":"1a8f346c-c9a0-444c-a1e2-189d70bcc22a","resolution":{"observed_at":"2026-08-04T17:16:16.100004Z","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":"2026-08-04T17:16:16.104536Z","title":"Deep multiple instance selection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.104536Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:eb2aa3d88456e4e5e4157d770e5654f1ab42d61c992782a51ca4455b835f523d","observation_id":"44cdabb5-58bf-448b-885f-f187749a9e49","resolution":{"observed_at":"2026-08-04T17:16:16.104536Z","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":"2026-08-04T17:16:16.108877Z","title":"Sparse multiple instance learning for elderly people balance ability,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.108877Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:f11d264c6c4e692d2dfaac8157a95f1ee74e6d38bdc0d81bbb8aa3a0f5a020f8","observation_id":"cf048dcc-f18e-40f0-a1a2-098c6d639dea","resolution":{"observed_at":"2026-08-04T17:16:16.108877Z","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":"2026-08-04T17:16:16.113327Z","title":"Primary mucinous ovarian tumors vs. ovarian metastases from gastrointestinal tract, pancreas and biliary tree: a review of current problematics,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.113327Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:fc8cd4c998d64ad227cb1ab723b650d7899c27a5c93f753125f5747d7e1bd292","observation_id":"3ac7e0e8-c859-48f8-8b44-be64937ed635","resolution":{"observed_at":"2026-08-04T17:16:16.113327Z","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":"2026-08-04T17:16:16.117703Z","title":"Pathology of primary and metastatic mucinous ovarian neoplasms,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.117703Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:4e3182e027076a8095a0f686966971b0865892964b1aaab96454d2670a5e0e1c","observation_id":"9541b7e5-a853-4801-b7da-d1557674ee55","resolution":{"observed_at":"2026-08-04T17:16:16.117703Z","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":"2026-08-04T17:16:16.122038Z","title":"Dtfd-mil: Double-tier feature distillation multiple instance learning for histopathology whole slide image classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.122038Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:4d8591777ff4f7e73f7dc3cc8b4659959a9f3445951655e75cf967932532cefa","observation_id":"92b137eb-cec5-466c-a873-55a3a08ef1df","resolution":{"observed_at":"2026-08-04T17:16:16.122038Z","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":"2026-08-04T17:16:16.126715Z","title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.126715Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:170cb958657c0391ca46734143e14ae21fae59818f41f94cba883560b110153d","observation_id":"c1182015-8a86-47c0-9ca1-3f6dc1417925","resolution":{"observed_at":"2026-08-04T17:16:16.126715Z","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":"2026-08-04T17:16:16.131082Z","title":"Solving the multi- ple instance problem with axis-parallel rectangles,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.131082Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:f9a3078b9bd383aafc67d739853880e55994c99662b6098e509e1e170029d660","observation_id":"9d39537e-36c1-4936-ac60-a5db97aa05b6","resolution":{"observed_at":"2026-08-04T17:16:16.131082Z","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":"2026-08-04T17:16:16.135941Z","title":"Multi instance neural networks,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.135941Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:01778e2e7e2dad27384a5c1bc282b9273cf8f66c48456438df4f6b60a116a569","observation_id":"bb49371e-9864-47d2-9faa-82cd8daa94fa","resolution":{"observed_at":"2026-08-04T17:16:16.135941Z","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":"2026-08-04T17:16:16.140985Z","title":"Key instance detection in multi-instance learning,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.140985Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:2c15afdb7c59a14f8e444cb34c0145cb80a70d8424c87b407f486197c3bc44e0","observation_id":"1108d786-461b-4961-ab2f-75015209cf3c","resolution":{"observed_at":"2026-08-04T17:16:16.140985Z","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":"2026-08-04T17:16:16.145439Z","title":"Multiple instance learning: A survey of problem characteristics and applications,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.145439Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:420605f01d31f13c2e9e1e95d1c57521291b1666a6b1d176e51d01d5899d42f0","observation_id":"7c412eb0-1639-4f8a-876d-3ab6baf73a68","resolution":{"observed_at":"2026-08-04T17:16:16.145439Z","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":"2026-08-04T17:16:16.149892Z","title":"From group to individual labels using deep features,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.149892Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:3987feb8094543097c9a9045fd56c0b586780cab24ea0a7519c69097759b4e15","observation_id":"4f2cba7a-198e-4b76-9ccd-90e8a3930afd","resolution":{"observed_at":"2026-08-04T17:16:16.149892Z","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":"2026-08-04T17:16:16.154297Z","title":"Support vector ma- chines for multiple-instance learning,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.154297Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:d11f79cfeb8e2463b112b6fab10d24a67d6e2e681d142c0a72ed1642ecd1d430","observation_id":"f196f990-b754-47b3-b89c-089066a4e668","resolution":{"observed_at":"2026-08-04T17:16:16.154297Z","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":"2026-08-04T17:16:16.159290Z","title":"Em-dd: An improved multiple-instance learning technique,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.159290Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:c263d8b126a651f20473291b23fec3a6498b98442f857a7184f4a23523538bee","observation_id":"e76b3631-f799-439b-8c32-09d5d6c921f6","resolution":{"observed_at":"2026-08-04T17:16:16.159290Z","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":"2026-08-04T17:16:16.163751Z","title":"Deep sets,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.163751Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:4563f5854a339f743b7a029eeea45127ed3683f7df740ee263ed48346e5a4481","observation_id":"56e81040-c659-4677-aac5-805efe25f9ad","resolution":{"observed_at":"2026-08-04T17:16:16.163751Z","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":"2026-08-04T17:16:16.168316Z","title":"Deep multiple instance learning for image classification and auto-annotation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.168316Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:c5eac8ad3f2ffc912e4e20d30791cdccbc58d476fcea1a402f9bcff66c5daf00","observation_id":"5cff285f-efc8-47f7-9ed8-3227e1247760","resolution":{"observed_at":"2026-08-04T17:16:16.168316Z","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":"2026-08-04T17:16:16.172792Z","title":"Trans- MIL: Transformer based correlated multiple instance learning for whole slide image classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.172792Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:ca55abd81d2e809d2e35e0e12e45d9e5e5ecc24a7910c7cb4491b0ee5f722a6f","observation_id":"1a27ea72-d27c-404c-ba77-482da435acbc","resolution":{"observed_at":"2026-08-04T17:16:16.172792Z","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":"2026-08-04T17:16:16.177607Z","title":"Multi-view multi-instance learning based on joint sparse representation and multi-view dictionary learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.177607Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:ca4d23768ba4b2dd4698d09f9f51c7ac2283071b5607bcfc1cc06e5fdf8039a0","observation_id":"ef2144d2-0a1b-48ba-a37e-5cb50876f25f","resolution":{"observed_at":"2026-08-04T17:16:16.177607Z","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":"2026-08-04T17:16:16.181851Z","title":"Weakly supervised object lo- calization with multi-fold multiple instance learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.181851Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:1db1d37fb67e7e6c7c7db650aea6fd8a723347495890e32730285f708be44b5d","observation_id":"27235e6a-89f7-4cda-b42f-ab16f2d2f462","resolution":{"observed_at":"2026-08-04T17:16:16.181851Z","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":"2026-08-04T17:16:16.186505Z","title":"Multi-layer multi-instance learning for video concept detection,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.186505Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:56a0f7a2fb5dacc1265edf2e36c95252f52f57402aa6b3c8011c80cd9099e213","observation_id":"04cd63d9-8029-4ad7-b479-a30e0f588665","resolution":{"observed_at":"2026-08-04T17:16:16.186505Z","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":"2026-08-04T17:16:16.190688Z","title":"Video anomaly detection with ntcn-ml: A novel tcn for multi-instance learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.190688Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:20ff3d3f3cf604c31069530bcccd3e1537689c10fd452dd24acac24251ca0695","observation_id":"0b079f78-9997-4f32-bd9f-9f761af3e42b","resolution":{"observed_at":"2026-08-04T17:16:16.190688Z","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":"2026-08-04T17:16:16.194811Z","title":"Text representation and classification based on multi-instance learning,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.194811Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:f6122d5e35e743c38bd1413f055265a7e66efa487e5536fb35e6cd0bfab79cff","observation_id":"b682d12c-867d-4abb-9d10-828418f6cf49","resolution":{"observed_at":"2026-08-04T17:16:16.194811Z","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":"2026-08-04T17:16:16.199247Z","title":"Multiple instance learning for classification of dementia in brain mri,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.199247Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:11ac1e15fcc703993cf29327016912cfc6cc22fdd5ec36dd54d44c2ead053188","observation_id":"bddd07b1-11a8-463b-bfa8-ac0b8e0d36a5","resolution":{"observed_at":"2026-08-04T17:16:16.199247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.02212","last_updated":"2020-02-20T16:25:47Z","snapshot_observed_at":"2026-08-16T13:09:18.319011Z","submitted_at":"2018-02-01T15:21:14Z","title":"Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.02212","snapshot_observed_at":"2026-08-04T17:16:16.203776Z","title":"Classification and disease localization in histopathology using only global labels: A weakly-supervised approach,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.203776Z"},"links":{"cited_paper":"/paper/1802.02212","citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:a1aeb66ddc12511734d12a87e8698368d49e4455bcc5f543b60b758eb0db6b30","observation_id":"d19d8031-ffa7-4f17-a262-18e05350b120","resolution":{"observed_at":"2026-08-04T17:16:16.203776Z","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":"2026-08-04T17:16:16.208506Z","title":"Whole slide images based cancer survival prediction using attention guided deep JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 12 multiple instance learning networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.208506Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:517dd1e41a682728485834e2c54b14ddcca45cdb08f449f41803bb7344dd2627","observation_id":"ed08e00f-c5f8-47e4-b49e-dc376461e292","resolution":{"observed_at":"2026-08-04T17:16:16.208506Z","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":"2026-08-04T17:16:16.213192Z","title":"Dual-stream multiple instance learn- ing network for whole slide image classification with self-supervised contrastive learning,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.213192Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:f130a645f2c1dae9f7c9b9d6df9c641e9734836a0af17b1c98b51df9bb1097a5","observation_id":"1129999d-1486-4af9-b995-03db7f0d5b3b","resolution":{"observed_at":"2026-08-04T17:16:16.213192Z","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":"2026-08-04T17:16:16.217734Z","title":"Murcl: Multi-instance reinforcement contrastive learning for whole slide image classification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.217734Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:2ea2323f68187a4d4929663424733f4b22b09b4377ecc7f2b79288a8f06fb752","observation_id":"c7c4fe6b-3866-40a7-9edb-6fdb23dafa1e","resolution":{"observed_at":"2026-08-04T17:16:16.217734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09476","last_updated":"2024-08-18T13:45:27Z","snapshot_observed_at":"2026-08-16T13:25:43.957441Z","submitted_at":"2024-08-18T13:45:27Z","title":"Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09476","snapshot_observed_at":"2026-08-04T17:16:16.222149Z","title":"Advances in multiple instance learning for whole slide image analysis: Techniques, challenges, and future directions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.222149Z"},"links":{"cited_paper":"/paper/2408.09476","citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:58720030f07afa5c980c7e1e0f0faac7fe3ce3e608f76ba3815e727f21f2357d","observation_id":"4232deed-888f-430a-9ae2-87e2925bc269","resolution":{"observed_at":"2026-08-04T17:16:16.222149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00351","last_updated":"2024-03-30T13:04:46Z","snapshot_observed_at":"2026-08-19T16:18:29.114749Z","submitted_at":"2024-03-30T13:04:46Z","title":"Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpoint","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00351","snapshot_observed_at":"2026-08-04T17:16:16.226681Z","title":"Rethinking attention-based multiple instance learning for whole-slide pathological image classification: An instance attribute viewpoint,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.226681Z"},"links":{"cited_paper":"/paper/2404.00351","citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:d276b61797863610fd9e6d8a04b8e6423a4328b8c2b84748ed6d07bef79da2dd","observation_id":"6daa266a-ac44-4183-ada7-af398a60e59d","resolution":{"observed_at":"2026-08-04T17:16:16.226681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.15303","last_updated":"2025-06-30T07:58:08Z","snapshot_observed_at":"2026-08-19T11:23:05.542231Z","submitted_at":"2024-06-18T02:01:17Z","title":"AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.15303","snapshot_observed_at":"2026-08-04T17:16:16.231751Z","title":"Aem: At- tention entropy maximization for multiple instance learning based whole slide image classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.231751Z"},"links":{"cited_paper":"/paper/2406.15303","citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:01f967842146aabf2cedc76184540006211ff1ad030ddb6ac2e2ce18f71d1eca","observation_id":"e0589a09-37ec-4338-af78-577ebd2a95e8","resolution":{"observed_at":"2026-08-04T17:16:16.231751Z","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":"2026-08-04T17:16:16.236482Z","title":"Rethinking multiple instance learning for whole slide image classification: A good instance classifier is all you need,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.236482Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:cb8d51ccb8f86382d89ef8a65532f2b03563310e61cee6434519dae0e2130d9e","observation_id":"cba53c7e-781c-43d4-91f5-cb269ffc0792","resolution":{"observed_at":"2026-08-04T17:16:16.236482Z","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":"2026-08-04T17:16:16.240911Z","title":"Attention is not what you need: Revisiting multi-instance learning for whole slide image classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.240911Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:40a2fe2c3e159709b52094f3f22ff2fee0a1c3cad004bb61fb7207aed501189e","observation_id":"5fa6bd7e-a9a6-401d-83b1-e6fb14953e0b","resolution":{"observed_at":"2026-08-04T17:16:16.240911Z","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":"2026-08-04T17:16:16.245215Z","title":"Hmil: Hierarchical multi- instance learning for fine-grained whole slide image classification,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.245215Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:47d58d828ac4f402ed9f451f90524c13bae4677a93629f76815fc2739854b76f","observation_id":"6ffd10f0-dff7-4934-8683-50af6f33acbb","resolution":{"observed_at":"2026-08-04T17:16:16.245215Z","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":"2026-08-04T17:16:16.249798Z","title":"Camil: channel attention-based multiple instance learning for whole slide image classification,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.249798Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:7db7391ff441e2fc382c7f19817d3f18bb706667fb0b9fba35f8b20d5aa6b8bb","observation_id":"eda2e056-c100-4abc-b615-60f883be7e89","resolution":{"observed_at":"2026-08-04T17:16:16.249798Z","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":"2026-08-04T17:16:16.254140Z","title":"Targeting tumor heterogeneity: multiplex- detection-based multiple instance learning for whole slide image clas- sification,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.254140Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:954b02812faedea0674ad3f2ae8f9e661ec6107fc93654cc452ca511cb7a48c6","observation_id":"480cee9a-7505-4ec5-8dab-a50435f99b9a","resolution":{"observed_at":"2026-08-04T17:16:16.254140Z","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":"2026-08-04T17:16:16.258367Z","title":"Camil: Causal multiple instance learning for whole slide image classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.258367Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:e79e1e2de1868e0e2b01ab7bafb947f6f2dc5e3abe0c800e88a55eae3c4a41bf","observation_id":"6a4d7d61-8f6b-446e-a48a-3f30c895fc28","resolution":{"observed_at":"2026-08-04T17:16:16.258367Z","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":"2026-08-04T17:16:16.262710Z","title":"Multiple instance learning with random sampling for whole slide image classification,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.262710Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:e4d689a247d3ae4e7f5511fbabc5963637f1367378059f9cce3f327b1147daef","observation_id":"ec166402-1a4a-4c09-a81f-f32a0bfbc184","resolution":{"observed_at":"2026-08-04T17:16:16.262710Z","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":"2026-08-04T17:16:16.266996Z","title":"Learning with ℓ1-graph for image analysis,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.266996Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:b8ba1ff4a7dc28e8aae643d295686281b03a4cd4d5a2faaf93010e3cd2d5b188","observation_id":"8a44f0cf-295e-4a04-babb-a685b44490fe","resolution":{"observed_at":"2026-08-04T17:16:16.266996Z","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":"2026-08-04T17:16:16.271393Z","title":"Multi-instance learning by treating instances as non-i.i.d. samples,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.271393Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:1f9097c31aaaff3975529a12dcf3427d3129eada3db526cfaa844f54502b69fb","observation_id":"b8e76ba8-09f5-4a1a-b3e9-28963837bda4","resolution":{"observed_at":"2026-08-04T17:16:16.271393Z","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":"2026-08-04T17:16:16.275903Z","title":"Adaptive prototype learning and allocation for few-shot segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.275903Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:2ec2a7ae6a5a36fb87c0a3031572744744b485f2c15aafca9d00cc3a0c529c24","observation_id":"62dacaa5-8a54-47e1-9b85-d89b27b61558","resolution":{"observed_at":"2026-08-04T17:16:16.275903Z","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":"2026-08-04T17:16:16.280315Z","title":"Enhancing sparsity by reweighted l 1 minimization,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.280315Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:f05de1f068692d029e0aaf7e1c73099ff4aad78a1c3613126ebe1633aa3e4124","observation_id":"441c2bf9-1725-4c07-a8fd-dd176bc68777","resolution":{"observed_at":"2026-08-04T17:16:16.280315Z","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":"2026-08-04T17:16:16.285022Z","title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.285022Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:a4dc5c08711a188db27aaced51eba6e19c362b16bcbbdf0cc399b4097f969944","observation_id":"354e6439-d640-46f5-9718-683e804ee535","resolution":{"observed_at":"2026-08-04T17:16:16.285022Z","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":"2026-08-04T17:16:16.289499Z","title":"Comprehensive molecular portraits of human breast tumours,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.289499Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:0b450a6a333ecc201f5ecb77bccda9a5aa02c0c3b36b3211edd395fc7f0a7b70","observation_id":"11748bfe-e48b-468b-b249-4fc42a21d1ad","resolution":{"observed_at":"2026-08-04T17:16:16.289499Z","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":"2026-08-04T17:16:16.294281Z","title":"Set trans- former: A framework for attention-based permutation-invariant neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T17:16:16.294281Z"},"links":{"citing_paper":"/paper/2509.11034"},"observation_digest":"sha256:f692f6e3e9ecf52f3a25d2cdc13faeb0fbec73f9650cb018db2d91971e07e8aa","observation_id":"482da0f3-e263-4b16-a883-d1d707b3408e","resolution":{"observed_at":"2026-08-04T17:16:16.294281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.11034","last_updated":"2025-09-14T01:50:51Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T13:10:04.351059Z","submitted_at":"2025-09-14T01:50:51Z","title":"Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":55,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":55},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2509.11034."}