{"as_of":"2026-08-17T19:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:83d7cd9bf41f050275ce2b08aa4c4cc7f2da25dd13668cd9f20a90d4c816169f","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:10:11.527373Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2506.19234/citation-record","integrity":"/paper/2506.19234/integrity","json":"/paper/2506.19234/citation-record.json","paper":"/paper/2506.19234"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:06.774164Z","title":"Deep industrial image anomaly detection: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:06.774164Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:c6c80072d0684070c0688336f6a2c2a8b8ecb6c92e61604e92f2438292859b18","observation_id":"7a31ca36-fc22-4bf3-af97-3b8bc53fe887","resolution":{"observed_at":"2026-08-06T23:10:06.774164Z","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":"2026-08-06T23:10:17.586787Z","title":"Machine learning for anomaly detection: A systematic review,","venue":null,"work_id":"de752e71-d454-48aa-bbb0-51fecad5d151","year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:06.851895Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:998fd79c231088dc270f72f8b7d36a6e1bf5fe2b3f6012013614523037876566","observation_id":"e6f3f77d-a3b4-4e2f-9973-e75d44a46a92","resolution":{"observed_at":"2026-08-06T23:10:17.726320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:17.411441Z","title":"Using an anomaly detection approach for the segmentation of colorectal cancer tumors in whole slide images,","venue":null,"work_id":"b83ffdfb-1083-4a72-85ed-0a79897de1a1","year":2023},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:06.959596Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:dd486393c4051c199b9c932d8e431792b0800c3db5ef9fc5af8a9c475397027c","observation_id":"398381ee-ab62-4611-9a8e-804ac5bbadbc","resolution":{"observed_at":"2026-08-06T23:10:17.497412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:17.282848Z","title":"Learning image representations for anomaly detection: application to discovery of histological alterations in drug development,","venue":null,"work_id":"6a1364f2-139e-4245-b322-86d3bdab9e3a","year":2024},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.058448Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:bc4d10d6efc95be19016a5f548aaea47e85314ed8d8a75191012839fd692719d","observation_id":"0282ece7-7aa8-4b59-b8a7-e5a4dba89927","resolution":{"observed_at":"2026-08-06T23:10:17.338944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:17.124133Z","title":"Automated anomaly detection in histology images using deep learning,","venue":null,"work_id":"ce455455-5f8b-4dac-ae6a-2a19de9e31bc","year":2024},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.118257Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:1d997d8f9d2d37ad9899bbba03ec70ae9e61f8dc4aebee2146b416dc9f6bc82c","observation_id":"f6b35fe9-e6f2-43ec-9aa4-bfb2dc269694","resolution":{"observed_at":"2026-08-06T23:10:17.185140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:16.898638Z","title":"A survey on unsupervised anomaly detection algorithms for industrial images,","venue":null,"work_id":"4fb92358-d70b-4b70-83e0-da39cc3b9492","year":2023},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.206355Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:434cdfa2ae24eba734f729eea2762c9966fd5f6d70258b979f6485f3c839757a","observation_id":"1ace07b6-23d2-4a6a-8522-2562d877201a","resolution":{"observed_at":"2026-08-06T23:10:16.958940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.16402","last_updated":"2024-01-29T18:41:21Z","snapshot_observed_at":"2026-08-16T14:23:32.148551Z","submitted_at":"2024-01-29T18:41:21Z","title":"A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16402","snapshot_observed_at":"2026-08-06T23:10:07.273869Z","title":"A survey on visual anomaly detection: Challenge, approach, and prospect,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.273869Z"},"links":{"cited_paper":"/paper/2401.16402","citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:4cbca14b948e5455bb5e1f5f8cc4069061d3d3b44644d5bc4240c61f5b2fbeb2","observation_id":"fb3c09f8-c92a-4682-9b20-820db08f938d","resolution":{"observed_at":"2026-08-06T23:10:07.273869Z","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":"2026-08-06T23:10:16.761561Z","title":"Medianomaly: A comparative study of anomaly detection in medical images,","venue":null,"work_id":"5c5ca55b-27d5-4835-957b-eba88f269fb1","year":2025},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.402648Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:efd25990756d45e598db8d8bcbe592c345fbaab1ce9a75d72aa177db150ac0e5","observation_id":"f857d157-bca7-4454-82a2-6c7732d9b2ba","resolution":{"observed_at":"2026-08-06T23:10:16.828514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:16.655589Z","title":"Unsupervised pathology detection: a deep dive into the state of the art,","venue":null,"work_id":"8dbc7e0d-4beb-45a2-82fc-a470718e95b4","year":2023},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.486539Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:4043ed4c256233d217917ac77dd899527166665cd5bf2d0fad5b9bfc26341536","observation_id":"6eac206e-ef00-4362-8018-47d2d60f5685","resolution":{"observed_at":"2026-08-06T23:10:16.696804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:16.532222Z","title":"Bmad: Benchmarks for medical anomaly detection,","venue":null,"work_id":"db72eba3-8b5f-47f5-ac84-666e0c46b20d","year":2024},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.564791Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:6c4f7bc6e277ab646bc770d87c2109a089187f414aec797f5654f9caef8aaeda","observation_id":"7cc687f8-13c6-4051-b3b8-270d258712a5","resolution":{"observed_at":"2026-08-06T23:10:16.590322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:07.666630Z","title":"A unified model for multi-class anomaly detection,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.666630Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:33779946a8efd10b425647c54898338307cff54a43d1b441b0b8fccb7db6882f","observation_id":"53bc383c-6d81-48d1-97b5-e16b589bb9ab","resolution":{"observed_at":"2026-08-06T23:10:07.666630Z","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":"2026-08-06T23:10:07.763654Z","title":"Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.763654Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:6b9a63ad93b60f9f842aa860b1789cd11ce473e7beb66b3a9b180497a8f1ed96","observation_id":"8d9c3941-d3b6-4049-8c0d-2ebfdedc6479","resolution":{"observed_at":"2026-08-06T23:10:07.763654Z","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":"2026-08-06T23:10:16.365771Z","title":"Deep one-class classifi- cation via interpolated gaussian descriptor,","venue":null,"work_id":"84f6fda9-821e-48e0-8ec9-446e314a1563","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:07.855322Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:6ce8b0b0ff4e04775da0ddf8fccaa3fed0531222a2a628e02458397407700c72","observation_id":"dc602954-6eca-4f69-b82f-55853b0196d8","resolution":{"observed_at":"2026-08-06T23:10:16.446561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:16.231567Z","title":"Denoising autoencoders for unsupervised anomaly detection in brain mri,","venue":null,"work_id":"02e2d678-28c2-4c3a-a13c-5ee31360f91c","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:08.002471Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:dc53e37f2ff1ff2c7b82f599edcf2f22be5e57c43a879d1a38e1ba5a09986dfa","observation_id":"6ae70bc9-33c9-4622-9e88-9d386455916b","resolution":{"observed_at":"2026-08-06T23:10:16.296874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:16.095084Z","title":"Constrained unsupervised anomaly segmentation,","venue":null,"work_id":"80be7299-35a5-4095-8cde-205a227458bb","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:08.349865Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:1e8b56fca966b80c767c457116228c099ef5b86de73470daee1b01d309074b8a","observation_id":"96fde5f4-69a2-483b-8278-9654ba606d5d","resolution":{"observed_at":"2026-08-06T23:10:16.173921Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:15.908564Z","title":"Ganomaly: Semi- supervised anomaly detection via adversarial training,","venue":null,"work_id":"0702f433-917c-4173-916e-e38fe0f68956","year":2018},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:08.597407Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:05a5a27717a5796dad368cfe2322133d74ebcdee506f11b373c99aabcd510df0","observation_id":"b1432ec4-b8e8-4238-99ab-0372897291de","resolution":{"observed_at":"2026-08-06T23:10:15.973811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:15.687989Z","title":"Unsupervised anomaly localization with structural feature-autoencoders,","venue":null,"work_id":"a8f1a9ba-53a1-49b4-b467-6ff1e6327763","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:08.699834Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:ce513982f044e4e58d31288047f039fa0ff3b49809c6a5a659a14e278e2a4716","observation_id":"2a1046d3-4315-4ec2-acf1-65e6d302cff0","resolution":{"observed_at":"2026-08-06T23:10:15.800835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.07122","last_updated":"2020-12-13T18:30:51Z","snapshot_observed_at":"2026-08-16T18:58:54.085481Z","submitted_at":"2020-12-13T18:30:51Z","title":"DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.07122","snapshot_observed_at":"2026-08-06T23:10:08.767273Z","title":"Dfr: Deep feature reconstruction for unsupervised anomaly segmentation,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:08.767273Z"},"links":{"cited_paper":"/paper/2012.07122","citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:5783914c1f4944c012099cefa538831767c2eaac06473701dc53b965e2d9ebe5","observation_id":"4d22d730-9193-4e16-9c37-32fa0c50fc26","resolution":{"observed_at":"2026-08-06T23:10:08.767273Z","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":"2026-08-06T23:10:15.567106Z","title":"Trans- former based models for unsupervised anomaly segmentation in brain mr images,","venue":null,"work_id":"e31d5b18-2772-4b5d-8179-aef823f37189","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:08.862533Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:0e9eccdc6cd46fabfc19a4ce04aaa8907d464274ac4e36dd2300703c151e47a7","observation_id":"cef77813-6475-457e-ba63-2116b42e4bed","resolution":{"observed_at":"2026-08-06T23:10:15.626799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:15.417865Z","title":"Panda: Adapting pretrained features for anomaly detection and segmentation,","venue":null,"work_id":"0027b71e-5bf3-42d1-91f7-6316fb71e12f","year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:08.931320Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:dd594b98bdcb7cb8f655c3e809e60975786e0da33d1f7e4e5ea432356ad18a2e","observation_id":"56241f9c-b960-4a5a-9c1b-b590bc5892ef","resolution":{"observed_at":"2026-08-06T23:10:15.498448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:15.271724Z","title":"Towards total recall in industrial anomaly detection,","venue":null,"work_id":"d65638cd-7c4e-415c-8e4a-418237527722","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.053028Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:e966cef1f9b76411d3c99bb93d07f33ae0b49374fcf567da55037f5265e75985","observation_id":"76b2afd5-4ddb-4883-9365-db35a98ef8ff","resolution":{"observed_at":"2026-08-06T23:10:15.337936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:15.138678Z","title":"Cfa: Coupled-hypersphere-based fea- ture adaptation for target-oriented anomaly localization,","venue":null,"work_id":"0a184894-efe5-41f7-b2d9-f72b9aa44769","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.148911Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:177a76af5a0c96c48195f35c3b353d8edc4ffb8efd409f534bf2c1ced2c4b9d8","observation_id":"42f340f0-d472-4dd5-8239-6ac8e3e5488f","resolution":{"observed_at":"2026-08-06T23:10:15.193787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:14.915526Z","title":"DFKDE - Anomalib Documentation,","venue":null,"work_id":"7f8bd325-5712-40ef-9b18-b38a9d85a854","year":null},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.217005Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:360fa54881ab69e25001a16683f42bde262be644226993e225b2abb4910eec87","observation_id":"6b2e7ee2-529c-4f6b-99a9-d7e7455a7e41","resolution":{"observed_at":"2026-08-06T23:10:15.026600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11786","last_updated":"2019-09-25T21:41:56Z","snapshot_observed_at":"2026-08-10T22:12:16.636212Z","submitted_at":"2019-09-25T21:41:56Z","title":"Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.11786","snapshot_observed_at":"2026-08-06T23:10:09.299107Z","title":"Probabilistic mod- eling of deep features for out-of-distribution and adversarial detection,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.299107Z"},"links":{"cited_paper":"/paper/1909.11786","citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:3a320b98bb698f19c02699fb8a6c13fe607de0ebbeb5a3ccfc5d42bfb8c08058","observation_id":"6804b814-83ee-4485-870f-5c6538818245","resolution":{"observed_at":"2026-08-06T23:10:09.299107Z","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":"2026-08-06T23:10:14.707475Z","title":"Padim: a patch dis- tribution modeling framework for anomaly detection and localization,","venue":null,"work_id":"2584f111-db11-48e6-a9bb-e64527fb687a","year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.400999Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:27adf0de9f494eaaa25ed8b346250dd080887290849612577fe30d2c4c72b8d0","observation_id":"fa14b223-038e-489b-9247-99ed22cb20c2","resolution":{"observed_at":"2026-08-06T23:10:14.787229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:09.504097Z","title":"Anomaly detection via reverse distillation from one-class embedding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.504097Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:4f5dae75db1534471ac0928384e007aed20a4a1c21196d5a3d9f6bf5233347ba","observation_id":"9ebe4a64-1537-4f78-9a2d-1808d5364a27","resolution":{"observed_at":"2026-08-06T23:10:09.504097Z","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":"2026-08-06T23:10:14.559686Z","title":"Revisiting reverse distillation for anomaly detection,","venue":null,"work_id":"4f2690f5-036b-4266-a106-830b51793d3a","year":2023},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.613578Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:db140b95a04a4a8b6915e4dbe0975461f0f02843bf3a79e4131734143f072a5f","observation_id":"67ea07d2-c8a1-48d7-9bce-23f36d549e49","resolution":{"observed_at":"2026-08-06T23:10:14.611331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.04257","last_updated":"2021-10-28T09:17:34Z","snapshot_observed_at":"2026-08-16T18:40:43.998497Z","submitted_at":"2021-03-07T04:25:04Z","title":"Student-Teacher Feature Pyramid Matching for Anomaly Detection","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.04257","snapshot_observed_at":"2026-08-06T23:10:09.695307Z","title":"Student-teacher feature pyra- mid matching for anomaly detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.695307Z"},"links":{"cited_paper":"/paper/2103.04257","citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:3c2959e5dc391748fb84ea75d9524a9b13f012b155d52a3778eea71a823fcf70","observation_id":"031d3db1-e059-47a5-8471-9e11712f13ac","resolution":{"observed_at":"2026-08-06T23:10:09.695307Z","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":"2026-08-06T23:10:14.369684Z","title":"Recontrast: Domain-specific anomaly detection via contrastive reconstruction,","venue":null,"work_id":"e89ad2f9-3522-4009-b107-27f99e35bd59","year":2023},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.792498Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:4fbc81a7b6b9bfe3c24d0f2d203d70fa332240b71da9e0cc6d0c2e75ff601783","observation_id":"0d522c3a-d8f1-43f9-ac33-ef8e76033fed","resolution":{"observed_at":"2026-08-06T23:10:14.437613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07677","last_updated":"2021-11-16T06:33:39Z","snapshot_observed_at":"2026-08-17T16:42:40.292350Z","submitted_at":"2021-11-15T11:15:02Z","title":"FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07677","snapshot_observed_at":"2026-08-06T23:10:09.903371Z","title":"Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.903371Z"},"links":{"cited_paper":"/paper/2111.07677","citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:b2037bc201bf50af4d197bce30e9b3760c7f174e29acf7446679e79ff08d7fe5","observation_id":"2f695de4-13b0-4895-b022-cd3a415239c8","resolution":{"observed_at":"2026-08-06T23:10:09.903371Z","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":"2026-08-06T23:10:14.171225Z","title":"Cflow-ad: Real-time unsu- pervised anomaly detection with localization via conditional normalizing flows,","venue":null,"work_id":"07113da6-ad45-4bf9-a738-960d32570d00","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:09.982962Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:4cd5a83e058c74b460b5c912229bd5d12e54f7efc689fd16739b15dfd9359e04","observation_id":"171e16ef-0075-4ed0-945d-672b861c932a","resolution":{"observed_at":"2026-08-06T23:10:14.241379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:14.013434Z","title":"Fully convo- lutional cross-scale-flows for image-based defect detection,","venue":null,"work_id":"28f7c865-abf2-46bf-9e23-20c90aac0c0c","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.076930Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:d579b917a0523bc6db039f9e071dfa21bc9db37e67a5f8791b77da25a0b432e6","observation_id":"7a6dbda3-254d-4481-b899-494baaecd693","resolution":{"observed_at":"2026-08-06T23:10:14.062470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:10.159442Z","title":"Cutpaste: Self-supervised learning for anomaly detection and localization,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.159442Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:dc8d38f883c2c01658f1482f98e8ed8a92329a6a1798203e074b36e6d928f925","observation_id":"8f968e44-3854-4b50-a151-9d59c09505d5","resolution":{"observed_at":"2026-08-06T23:10:10.159442Z","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":"2026-08-06T23:10:13.800126Z","title":"Anomaly detection in medical imaging with deep perceptual autoen- coders,","venue":null,"work_id":"beef4937-1710-414f-b731-b4adaef4b18c","year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.255715Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:d21215050872f8f6b8ee272dfb908695d125f4e327036ec196c0c6127919085c","observation_id":"6d1cda12-f58d-41e7-9c2c-ee5d40b727ce","resolution":{"observed_at":"2026-08-06T23:10:13.928302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:13.605475Z","title":"Multiscale generative model using regularized skip-connections and perceptual loss for anomaly detection in toxicologic histopathology,","venue":null,"work_id":"d32e1906-cc3e-48f8-8b6a-01b34b63d354","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.324999Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:76225eba01ee24f1320a5cca1de9fc81a814f5b3dd998ba0436913cde901951e","observation_id":"bac2a3c3-dc79-4c53-afab-e4af9a6d1e91","resolution":{"observed_at":"2026-08-06T23:10:13.665052Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:13.376516Z","title":"Unsupervised anomaly detection in digital pathology using gans,","venue":null,"work_id":"3292b241-84d6-4f8d-86b6-3ea7244ce2bb","year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.416860Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:ae00d0183a709fdcb504045bbc2ea0848d0fcf2262dc0b90200713dd495aef3b","observation_id":"ec43cdd1-46d9-4163-9da1-12349518cb76","resolution":{"observed_at":"2026-08-06T23:10:13.510176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:10.513811Z","title":"Perceptual losses for real-time style transfer and super-resolution,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.513811Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:05e2180e9a8f94dc35384439690ccf20667b901e7f57cfad7b1adee050d4992c","observation_id":"59d0ac66-65fe-4c18-9f3d-0a2b52c19381","resolution":{"observed_at":"2026-08-06T23:10:10.513811Z","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":"2026-08-06T23:10:13.183521Z","title":"Ganomaly: Semi-supervised anomaly detection via adversarial training,","venue":null,"work_id":"3fb63f6e-2459-4b71-a2d3-fe522b2fd01d","year":2018},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.605563Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:d1469eb785c473865ccc91af698f1941db32297b11ae1be63af52b5dc1b0c4a1","observation_id":"44c08e30-0ce9-4b89-b999-df1bd8551901","resolution":{"observed_at":"2026-08-06T23:10:13.267236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:13.005097Z","title":"Unsupervised anomaly detection on histopathology images using adversarial learning and simulated anomaly,","venue":null,"work_id":"0c81ea14-59e1-4ced-954b-02e537b29611","year":2024},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.706325Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:2d8fc64dc11a5919a4881835799b39f02e1f0b8d3849c712d32892b49717bb55","observation_id":"4dde7b0f-5331-4e15-844c-a8a4155d7572","resolution":{"observed_at":"2026-08-06T23:10:13.066209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:10.840157Z","title":"Diffusion models for out-of-distribution detection in digital pathology,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.840157Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:27da81a373cfe957ffde94efa59458be87ad56a38e0e435f3218393a7ab41914","observation_id":"ceda4458-a22c-437f-ae88-6ed99e1fd769","resolution":{"observed_at":"2026-08-06T23:10:10.840157Z","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":"2026-08-06T23:10:12.800486Z","title":"1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset,","venue":null,"work_id":"7b7fc64e-db8c-48d6-bbcf-a7e1eca225c1","year":2018},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.909563Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:606e8429e2b53d089d2650b42dc449b5e5edd52fbd829bef0d6c788195890d62","observation_id":"f0dd2897-6724-4f73-b5cc-c2a94282feb4","resolution":{"observed_at":"2026-08-06T23:10:12.891659Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:12.594986Z","title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer,","venue":null,"work_id":"cee014f4-6df1-40be-8676-b22a065e9715","year":2017},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:10.993548Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:a63c0fc7ce46745da05b9209875eddd9b8582557808016699a8dcfc9002ba501","observation_id":"e325a533-6671-460c-b064-0576b738277b","resolution":{"observed_at":"2026-08-06T23:10:12.688242Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:12.399864Z","title":"A cross-platform informatics system for the gut cell atlas: integrating clinical, anatomical and histological data,","venue":null,"work_id":"2d8f1824-23bb-4c09-bf94-75ac99581134","year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:11.071455Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:64c20055d358a919893cd25597856966e470783fca3b4b59947757f4bfc7904f","observation_id":"678796e2-3bdd-4eab-aebc-5e0b21564d9a","resolution":{"observed_at":"2026-08-06T23:10:12.501409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:12.211521Z","title":"Glo-in-one: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining,","venue":null,"work_id":"69f55717-f030-4e2a-86f8-95e9eb5dbc99","year":2022},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:11.161832Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:e99def262e88aaaa3f987cb79e01dd6c4c0047810fc0a81ab5c2d2c43d27f237","observation_id":"d049223e-39bf-4fcc-9be6-3669828bb4a1","resolution":{"observed_at":"2026-08-06T23:10:12.297030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:12.044523Z","title":"Unitopatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading,","venue":null,"work_id":"b3cc3cbf-04ff-4166-b146-96bac752908b","year":2021},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:11.273229Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:836965d8287ba624fc306089cacc488bff0fc2d58bf0e2e6ed86091e55e85e5f","observation_id":"257c8338-65c7-4490-a20b-32efe1066930","resolution":{"observed_at":"2026-08-06T23:10:12.115050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:11.355561Z","title":"Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:11.355561Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:058b34ffe25cd5d02e359a82c0a3ce093337cf2d6459faeab2d8c29320eefab0","observation_id":"7e5459c4-da0c-407b-808a-903c1dcddc57","resolution":{"observed_at":"2026-08-06T23:10:11.355561Z","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":"2026-08-06T23:10:11.841898Z","title":"Feasibility of universal anomaly detection without knowing the abnormality in medical images,","venue":null,"work_id":"a8da4b18-0b8b-4ed3-b671-a0f31b7ac878","year":2023},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:11.422369Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:19e01bc58929507f3222c1cb88670c89c5b4c5de565dad95436f8afd2ec9894c","observation_id":"e6c6417d-e1f3-4066-b6ef-4cb76e561242","resolution":{"observed_at":"2026-08-06T23:10:11.945133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:10:11.668623Z","title":"Skip-ganomaly: Skip connected and adversarially trained encoder-decoder anomaly de- tection,","venue":null,"work_id":"2965da7f-090c-461b-a92b-cc8bb6128738","year":2019},"citing_paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T23:10:11.527373Z"},"links":{"citing_paper":"/paper/2506.19234"},"observation_digest":"sha256:b4c067faec446c20a03577031db747dfa45b8466659d57443797cc7542cf6339","observation_id":"5d958326-7810-4426-9481-7d218a1c357e","resolution":{"observed_at":"2026-08-06T23:10:11.737906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.19234","last_updated":"2025-06-24T01:39:23Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-14T21:50:58.505473Z","submitted_at":"2025-06-24T01:39:23Z","title":"Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":35},"total_outbound_references":48},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2506.19234."}