{"as_of":"2026-08-10T12:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:711e9549a6928f687f9c327c4f80d2e4af4a2747dc3f8dd3b0d052f0161d592f","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:04:31.782629Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2505.22855/citation-record","integrity":"/paper/2505.22855/integrity","json":"/paper/2505.22855/citation-record.json","paper":"/paper/2505.22855"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:04:26.381520Z","title":"Three types of incremental learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:26.381520Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:25e1a6a281ded1ff3860a8a69833137472df6352ced8781e3acde95e51010d3f","observation_id":"63d8af83-a90e-49b5-b7fa-894dc2745922","resolution":{"observed_at":"2026-08-07T13:04:26.381520Z","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":"2026-08-07T13:04:38.445188Z","title":"Ella: An efficient lifelong learning algorithm,","venue":null,"work_id":"ab9907b0-d4e6-45a4-9b0f-a5788b73ea3f","year":2013},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:26.456549Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:eb27c73b3feb945a61ee7056994b459838abae0e594e01e1628def14056c3b6d","observation_id":"3e5dfb43-eb5e-40b4-841a-edfb24924740","resolution":{"observed_at":"2026-08-07T13:04:38.540198Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:38.343565Z","title":"Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization,","venue":null,"work_id":"b75e471c-c5bd-44e3-af5e-c017eb9af59f","year":2018},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:26.573738Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:e3132b191960ebcd17638942bbacddddd5fe77573119950e4ec0234a59107229","observation_id":"c182c6af-bd6b-46bc-8b89-99e1b61696c7","resolution":{"observed_at":"2026-08-07T13:04:38.392127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03027","last_updated":"2022-06-15T22:16:33Z","snapshot_observed_at":"2026-08-09T07:56:34.809736Z","submitted_at":"2021-06-06T04:25:09Z","title":"Model Zoo: A Growing \"Brain\" That Learns Continually","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.03027","snapshot_observed_at":"2026-08-07T13:04:26.661886Z","title":"Model zoo: A growing","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:26.661886Z"},"links":{"cited_paper":"/paper/2106.03027","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:fe02f269f4fad4c8266843476657bf7b99627fdee43e2bc906b0ac6e06bbd1f8","observation_id":"d55785c5-dc4b-4eea-a941-8517e6fc0afa","resolution":{"observed_at":"2026-08-07T13:04:26.661886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02714","last_updated":"2021-12-05T23:55:53Z","snapshot_observed_at":"2026-08-10T05:03:53.658036Z","submitted_at":"2021-12-05T23:55:53Z","title":"CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.02714","snapshot_observed_at":"2026-08-07T13:04:26.828847Z","title":"Classic: Continual and con- trastive learning of aspect sentiment classification tasks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:26.828847Z"},"links":{"cited_paper":"/paper/2112.02714","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:0d68b52ac33afd652733031d0485c4cb6ce5750cf6147314d27ae7058b6c74cc","observation_id":"0ead0f5c-989c-4f89-8ae2-543f184772bc","resolution":{"observed_at":"2026-08-07T13:04:26.828847Z","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":"2026-08-07T13:04:38.124569Z","title":"An efficient domain-incremental learning approach to drive in all weather condi- tions,","venue":null,"work_id":"74b57e69-7031-421c-a373-0db2d2e22abb","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:26.970063Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:be8ed48ca5a9a9dafec8c657d9b823823e9fb1662cd5bd6596eef425fa8e656f","observation_id":"d16bf2aa-c9a8-48a7-ae83-e7dd589d238d","resolution":{"observed_at":"2026-08-07T13:04:38.229057Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.00695","last_updated":"2022-04-11T14:58:15Z","snapshot_observed_at":"2026-08-06T22:48:15.827343Z","submitted_at":"2019-06-03T10:45:08Z","title":"Continual learning with hypernetworks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.00695","snapshot_observed_at":"2026-08-07T13:04:27.048240Z","title":"Continual learning with hypernetworks,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.048240Z"},"links":{"cited_paper":"/paper/1906.00695","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:0d96bcc5b10dd30bb2a89ec139a2e8a872a420ed45d09923923411227cec59be","observation_id":"938bad9f-b34b-4ccb-ac4a-89a8e95629bf","resolution":{"observed_at":"2026-08-07T13:04:27.048240Z","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":"2026-08-07T13:04:27.136818Z","title":"Continual learning with deep generative replay,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.136818Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:3643848ac892ff7939da635927df1c1a346c7e10d617aaf10b61c324ff45ad97","observation_id":"862b0bbf-6320-4a46-a16a-c8583788f65f","resolution":{"observed_at":"2026-08-07T13:04:27.136818Z","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":"2026-08-07T13:04:37.860727Z","title":"Brain-inspired replay for continual learning with artificial neural networks,","venue":null,"work_id":"7cb8548a-a2fb-46b3-bcd7-bb2c5ac32b7f","year":2020},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.217916Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:de5b54da37cbd28c1b0775c966a9e53c3f47f759b58272d6adf24dd9310ce8bc","observation_id":"a967a8e5-6e1a-45a6-a804-ae58f54e7ea3","resolution":{"observed_at":"2026-08-07T13:04:37.999801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:37.778760Z","title":"A systematic collection of medical image datasets for deep learning,","venue":null,"work_id":"33942d7e-ee3d-4e59-869c-0052f32cf501","year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.297854Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:ca22f78463f72d6cbea246c5f3d5cd08e12c68a139135c64b0826c5a701745d7","observation_id":"286f97da-8aa7-4d92-87dc-dcbb184c3d8c","resolution":{"observed_at":"2026-08-07T13:04:37.855915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:37.575752Z","title":"A systematic approach of data collection and analysis in medical imaging research,","venue":null,"work_id":"1de10280-140b-4e50-9422-b71c5a571829","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.366182Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:9155e5b7de9bea64fa83ffb53843c78a6f6a1d2e4791c21046323a55fd59ab58","observation_id":"2e048caf-a34a-47a7-a23b-181e4e9ac4fd","resolution":{"observed_at":"2026-08-07T13:04:37.668185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:37.372714Z","title":"Continual learning in medical devices: Fda’s action plan and beyond,","venue":null,"work_id":"7649de62-2188-45aa-bd5a-1c7b145343bb","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.438588Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:f96968701c203ec0adfc9d6aad086493cbb65d89168b9b384b878a84f376dec1","observation_id":"c7f9bdb6-5489-4176-b606-bf54baf1c18c","resolution":{"observed_at":"2026-08-07T13:04:37.493019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:37.214017Z","title":"Passive data collection and use in healthcare: A systematic review of ethical issues,","venue":null,"work_id":"fd043ad7-f966-4f3e-9f3a-e73930a25802","year":2019},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.484985Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:e5ec32883bd47e5b38105374d1d9e3ad60d167c0744f3aa21fd4ee1b6766b1d6","observation_id":"44db5b8c-c581-44f4-87db-6e09b8e8c5cc","resolution":{"observed_at":"2026-08-07T13:04:37.278362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:37.080635Z","title":"Data collection theory in healthcare research: the minimum dataset in quanti- tative studies,","venue":null,"work_id":"df44de38-09f4-435f-97b6-29390bd61acc","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.568320Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:8b26ca104aac72b00704af9a08ad43c3f845a27a1286376a69fcb7727807f93f","observation_id":"54d879f1-d941-420d-b435-12bd44225d3c","resolution":{"observed_at":"2026-08-07T13:04:37.158295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:36.980040Z","title":"Towards flexible mobile data collection in healthcare,","venue":null,"work_id":"6bfea027-c695-45eb-9bbb-dfdf8fdbadd2","year":2016},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.641935Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:ac25179570a6afa4a4bfeb90f392ebeeaaf4c93db37169e99f81a2166be53423","observation_id":"3989115d-c174-4d76-b154-857297ad9de7","resolution":{"observed_at":"2026-08-07T13:04:37.014802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:36.826279Z","title":"Digital pathology and computational image analysis in nephropathol- ogy,","venue":null,"work_id":"54fa5565-1eed-47ef-bf81-c25eb966c7bd","year":2020},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.709004Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:d0601ec195d432e71d6aa12bd6e4dfb9ef70c8ea89087a7d2dded79afe515535","observation_id":"83038a1e-34df-45c3-b5d2-6efdbd7642ce","resolution":{"observed_at":"2026-08-07T13:04:36.893780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:36.690807Z","title":"Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging,","venue":null,"work_id":"63423c93-bc87-4a51-9813-fc91bb906860","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.783557Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:15afd88faf98c9a7f182c72a5c8dfc1e4456f78ee82e825360d8193b86403c9d","observation_id":"76f28544-f675-4026-a4ad-ee7fd9460887","resolution":{"observed_at":"2026-08-07T13:04:36.776525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:36.576920Z","title":"Continual learning for abdominal multi-organ and tumor segmentation,","venue":null,"work_id":"c1d7fac4-7940-420f-96fb-b4e197bbaf24","year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.853331Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:c3f72197d78734de564a3e39fcacd6aba5889d730760ab10893a9889949fc565","observation_id":"89c865be-1fe3-45c6-9ce2-92bca6632f1f","resolution":{"observed_at":"2026-08-07T13:04:36.637597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:36.431268Z","title":"Low- rank mixture-of-experts for continual medical image segmentation,","venue":null,"work_id":"945ec295-11a5-4bd0-8f4d-8934d4af6d61","year":2024},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.920484Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:be70df6b3d6c44e2312f715be2502a8a943cbeb5ea4b75c2000feab5be687555","observation_id":"a7db80be-479a-4ef7-b25c-ba055b4802a0","resolution":{"observed_at":"2026-08-07T13:04:36.476830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:36.294026Z","title":"Lifelong nnu-net: a framework for standardized medical continual learning,","venue":null,"work_id":"502254a3-0775-4ece-b390-f09353a45b8a","year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:27.986945Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:8c51f23775c1730f4ac7b62a7dd8779b7491d2fe05b403850c27c80cafd2829b","observation_id":"774f00d9-10e8-4333-991d-1a3c17a971eb","resolution":{"observed_at":"2026-08-07T13:04:36.383911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:28.068191Z","title":"Deep hierarchical semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.068191Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:8f6bc097e48bdad84d4100784d20ecbc8dea3d02f51897e89ad14a13ccce1621","observation_id":"6bfdbb00-a3f5-4ec0-8b61-772315113c7b","resolution":{"observed_at":"2026-08-07T13:04:28.068191Z","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":"2026-08-07T13:04:36.174666Z","title":"Unsupervised hierarchical semantic segmentation with multiview cosegmentation and clustering transformers,","venue":null,"work_id":"d258f936-e81f-47c7-8c71-964e137e77ec","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.162888Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:097aaabd779b84db34472c7bb3e660094d43fd5880b8abdb21490fb808cf352e","observation_id":"7ee7a9be-be86-4b2d-9e0e-16ad67c76e8a","resolution":{"observed_at":"2026-08-07T13:04:36.223358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:35.999158Z","title":"Prpseg: Universal proposition learning for panoramic renal pathology segmentation,","venue":null,"work_id":"df201b4c-585b-4dee-9066-e199811cbb00","year":2024},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.259165Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:28fdc3b5e273ff710964633b72884a52b6c91b15763327d950286ad8868fe522","observation_id":"45941d5a-146c-4112-8073-e20ae74ff02e","resolution":{"observed_at":"2026-08-07T13:04:36.093176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:35.876326Z","title":"Artificial intelligence in renal pathology: Current status and future,","venue":null,"work_id":"be5e4271-1e44-4e13-b215-60277a9c6010","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.358365Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:e2d82382bad6b100bea63d59e963d4584556031fdda3d690d24deee029fb0cea","observation_id":"e28aebb8-20c1-4bf6-9f05-05ccbcf9d34a","resolution":{"observed_at":"2026-08-07T13:04:35.942686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:35.718560Z","title":"Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules,","venue":null,"work_id":"a7131ace-bfcb-41cb-be8d-ff2d713bd55e","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.457160Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:96c60e2922c329b157b7fb1dc19a1028350126e548a0b3d300c9b0ff2ce98a73","observation_id":"d09dc920-47b6-436a-93f7-2b25b34a90c1","resolution":{"observed_at":"2026-08-07T13:04:35.793460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.00251","last_updated":"2017-08-01T11:13:04Z","snapshot_observed_at":"2026-07-06T05:53:32.920721Z","submitted_at":"2017-08-01T11:13:04Z","title":"CNN Cascades for Segmenting Whole Slide Images of the Kidney","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.00251","snapshot_observed_at":"2026-08-07T13:04:28.520253Z","title":"Cnn cascades for segmenting whole slide images of the kidney. arxiv 2017,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.520253Z"},"links":{"cited_paper":"/paper/1708.00251","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:5924849b31e8c7b540c1eb4af5ce00f50bd58fad94bf5f4b2d0813f34d1be5e5","observation_id":"fd4ee40c-2431-425d-9852-20e4be6bf690","resolution":{"observed_at":"2026-08-07T13:04:28.520253Z","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":"2026-08-07T13:04:35.567682Z","title":"Glomerulus classification and detection based on convolutional neural networks,","venue":null,"work_id":"c05602e7-11d7-4334-a47b-2c406016e1e2","year":2018},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.591029Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:50ec34168604d799984b0c3520921d062cb638fb702b13f4053da0538313d8fb","observation_id":"79f938d0-a087-4ff1-863f-13e56be6cb9f","resolution":{"observed_at":"2026-08-07T13:04:35.609594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:35.394034Z","title":"Glomerulosclerosis identification in whole slide images using semantic segmentation,","venue":null,"work_id":"782997ac-31be-446e-9bd7-41bec3095441","year":2020},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.689151Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:198c7c9bf7933032d178ccc4b24f7b11a3b937ef1436d43e4f6aab816572a07a","observation_id":"8708e80d-2b17-416d-b02b-fb2c9cd834db","resolution":{"observed_at":"2026-08-07T13:04:35.503577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:35.238227Z","title":"An integrated iterative annotation technique for easing neural network training in medical image analysis,","venue":null,"work_id":"d756fa29-5b3e-4bc9-81f2-d362ae3b5b5c","year":2019},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.763576Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:89340713510119f7563ca0a30e8ab48cc00e3b3a2400e6c79a1c4b396d800325","observation_id":"9bf7dbdb-7178-47f7-8816-c1db72682742","resolution":{"observed_at":"2026-08-07T13:04:35.349686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:35.110085Z","title":"Iterative learning to make the most of unlabeled and quickly obtained labeled data in histology,","venue":null,"work_id":"1e7caf87-efb4-4446-9516-a2e1f445f1ea","year":2018},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.824614Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:17e64edd9a8ccee51b65ae223c1e9be08c1aa4a4d46571b9bdf721b903a8f9be","observation_id":"88eea743-7cad-4c02-ad30-c96917ec6c9d","resolution":{"observed_at":"2026-08-07T13:04:35.182119Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:34.944112Z","title":"Segmentation of glomeruli within trichrome images using deep learning,","venue":null,"work_id":"08317568-1e7f-4a28-9f70-cb1829e93805","year":2019},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.881532Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:66f9aea6e80445930bff1d6bd4592950d160b036fc9f25a75dfbbae36f8527ea","observation_id":"92df080e-f516-482f-9528-e1e343323dac","resolution":{"observed_at":"2026-08-07T13:04:35.038790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:34.808939Z","title":"Automatic nucleus segmentation with mask-rcnn,","venue":null,"work_id":"6e63126a-0f26-4b32-ad39-5098d57cfbbd","year":2019},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:28.950739Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:b8cc11339247a68d5c0ba51bff557ccb2491e438f485fc0e63c9e35c2dcc6996","observation_id":"c380c721-088e-477f-a5ae-334993ed9281","resolution":{"observed_at":"2026-08-07T13:04:34.858930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.11993","last_updated":"2021-09-22T15:18:55Z","snapshot_observed_at":"2026-08-10T03:47:42.013266Z","submitted_at":"2021-08-26T18:46:43Z","title":"Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation","version":2},"cited_work":{"arxiv_id":"2108.11993","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.11993","snapshot_observed_at":"2026-08-07T13:04:31.959822Z","title":"Evaluating Transformer-based Semantic Segmentation Networks for Pathological Image Segmentation","venue":"eess.IV","work_id":"4cb6989f-8723-42e8-80c2-cb1bb96bd198","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.026459Z"},"links":{"cited_paper":"/paper/2108.11993","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:bb021020f950f62f3aaaa5fc3dbdf8288106a5bda57f68f3559d824730d6c24f","observation_id":"37e9b4fe-418c-4be7-943a-faf356bd0bf5","resolution":{"observed_at":"2026-08-07T13:04:32.069534Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:34.653751Z","title":"Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image,","venue":null,"work_id":"4246de5e-80e3-4c37-8021-d3728ff073e7","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.116415Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:09af1e624e91c9a2fe041f53d40e8a0437d3ec61d6516609a227f3708918f9a2","observation_id":"a450754b-6238-489e-9fb2-cb00dbb24541","resolution":{"observed_at":"2026-08-07T13:04:34.754006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:34.462706Z","title":"Self reinforcing multi-class transformer for kidney glomerular basement membrane segmentation,","venue":null,"work_id":"1655de15-879f-44b6-8341-8cbd85a9f839","year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.181024Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:913e42988fe43544309a25ca150b7b696f7289e860dafc0825d9eaf0fda08951","observation_id":"0aa6c4f9-45a8-4256-9366-9500aad78828","resolution":{"observed_at":"2026-08-07T13:04:34.549808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:34.312160Z","title":"Automatic evaluation of histological prognostic factors using two consecutive convolutional neural networks on kidney samples,","venue":null,"work_id":"63707c86-a671-42ce-922b-b5a8a78ccb36","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.254716Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:b20df65eef03ff1b186c6a2e541e43e9fb8dcb32dfa3dedc200d3b23fb3ac642","observation_id":"18ce508b-6aae-4c57-9b84-8f28422e706d","resolution":{"observed_at":"2026-08-07T13:04:34.390740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:34.205283Z","title":"Multi-structure segmentation from partially labeled datasets. application to body compo- sition measurements on ct scans,","venue":null,"work_id":"4354f6a8-6df2-4d01-b3d9-db35d88c558f","year":2018},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.321253Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:8e0a95c867a0ae6d6689712cb36182a77a1c79f8d691019ee7e1449486e21130","observation_id":"363ce123-c9b0-4170-807a-ca941e86d245","resolution":{"observed_at":"2026-08-07T13:04:34.249080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:34.052539Z","title":"Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction,","venue":null,"work_id":"347600c0-52ad-4ed5-85e1-1c7dbaff370a","year":2020},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.372588Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:aa0c8b479f77566d48033f21ff0d2007e47ad1c7be0a50325ffebc7bede9fba4","observation_id":"c2a22b41-39f9-473b-9064-cbf9ca839a13","resolution":{"observed_at":"2026-08-07T13:04:34.124877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:33.894990Z","title":"Deep learning–based segmentation and quantification in experimental kidney histopathology,","venue":null,"work_id":"847feb80-12a7-42ca-9d98-4d66ea4c98cf","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.472601Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:f8f2c9225eb76e1621e1933c2bc0b60b0820c6aad6fa0be2bf607bdc1496b623","observation_id":"abb0677a-2670-4272-b51f-9b2dcac5a787","resolution":{"observed_at":"2026-08-07T13:04:33.956156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.00625","last_updated":"2019-07-17T10:19:12Z","snapshot_observed_at":"2026-08-06T19:43:14.274077Z","submitted_at":"2019-04-01T08:14:29Z","title":"Med3D: Transfer Learning for 3D Medical Image Analysis","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.00625","snapshot_observed_at":"2026-08-07T13:04:29.533178Z","title":"Med3d: Transfer learning for 3d medical image analysis,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.533178Z"},"links":{"cited_paper":"/paper/1904.00625","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:86b3a8774e4307e38976f28d26e9ae9617b3d005b47855541580a67858916c0d","observation_id":"180a82c9-a2a7-4b41-a416-af3e9fdb15e3","resolution":{"observed_at":"2026-08-07T13:04:29.533178Z","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":"2026-08-07T13:04:33.773292Z","title":"Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets,","venue":null,"work_id":"27d44921-9ebf-4118-90d8-c2852bf47f6a","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.591383Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:d7b2257c537936fed3c1f44ae23e1c5f50229950645e6172f92d779d3bf2e26e","observation_id":"0913a1a8-dfec-45d4-8d14-158f488d352a","resolution":{"observed_at":"2026-08-07T13:04:33.839079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:33.614175Z","title":"Omni-seg: A scale-aware dynamic 10 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2025 network for renal pathological image segmentation,","venue":null,"work_id":"aa4699a7-54a6-47f4-abac-108c23deea2a","year":2025},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.661684Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:ae6f0d2c5b43f67f8ea23d131a4d4f5369a438bcb71270a6936dc6b376ebb156","observation_id":"c5894c67-3699-490d-a5a4-2d8fff4bf5fc","resolution":{"observed_at":"2026-08-07T13:04:33.689157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:33.434586Z","title":"Segmentation of tumour regions for tubule formation assessment on breast cancer histopathology images,","venue":null,"work_id":"c3ad2b2d-a979-4a5c-8353-7bf8404561d1","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.706639Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:ff890ed4d97daf4c7aaa55a078007160cc897a409e493645ec92713724b93467","observation_id":"e6eec956-b750-4f2d-9ff7-83fefe9685b1","resolution":{"observed_at":"2026-08-07T13:04:33.539197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:33.345669Z","title":"Adversarial learning with data selection for cross-domain histopathological breast cancer segmentation,","venue":null,"work_id":"f9fc6d2f-d14a-4f8b-93ac-0911afe3adb7","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.848722Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:940803e736f8cac7830a36b594f001747d349174bf1ed972957ac6f0fd0f9b69","observation_id":"bb268798-ef7c-4d5a-a2f0-d10f1792b98c","resolution":{"observed_at":"2026-08-07T13:04:33.387425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:33.239499Z","title":"Muscle: Multi-task self-supervised continual learning to pre-train deep models for x-ray images of multiple body parts,","venue":null,"work_id":"dd8b7d00-d814-424f-a8e9-d6686bf6a5f3","year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:29.980838Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:d17ce7208a8759820d68979f43fcb7cec266a6a3a14eefd91e79728e21926da6","observation_id":"d01405d0-a507-48de-aabd-a243f6bfbaac","resolution":{"observed_at":"2026-08-07T13:04:33.282822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:30.069898Z","title":"Universeg: Universal medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:30.069898Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:6a70235f2f8035ab27d312e468d52a2c47eed68198e2e78568f968e5fe9cfeaf","observation_id":"2e86a629-2b66-4424-a05b-638e91f39a07","resolution":{"observed_at":"2026-08-07T13:04:30.069898Z","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":"2026-08-07T13:04:33.059697Z","title":"Continual self-supervised learning: Towards universal multi-modal medical data representation learning,","venue":null,"work_id":"c6f3d826-82fd-46ca-80f3-09ee440aba2e","year":2024},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:30.181737Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:d70ca4e67a02a139d4c6fc5f97631cbc50f4a110231370228018dfce8a6eca09","observation_id":"1d85edef-7eb7-448d-b3d6-896a15e188b0","resolution":{"observed_at":"2026-08-07T13:04:33.128703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:32.914557Z","title":"Continual segment: Towards a single, unified and non- forgetting continual segmentation model of 143 whole-body organs in ct scans,","venue":null,"work_id":"0d69e396-d647-4234-9272-e940378d82c6","year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:30.314530Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:938385841fc04e5bc2302c1b0188a0b3989a14f537b05c485c01bb8649d0fb7f","observation_id":"02ddefe3-15b0-4381-bb99-63dead05b341","resolution":{"observed_at":"2026-08-07T13:04:32.980944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:32.809549Z","title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,","venue":null,"work_id":"6340ace1-04c3-4817-ac4d-724685e4fb01","year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:30.483399Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:9075149e221f413c537a2b6db345417f3c4eb45a1389dc38088c6b0526562d9d","observation_id":"1f55c544-9f14-4dcc-b178-a0ee8e7513b2","resolution":{"observed_at":"2026-08-07T13:04:32.853201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00863","last_updated":"2023-12-01T18:31:00Z","snapshot_observed_at":"2026-07-06T16:55:49.813116Z","submitted_at":"2023-12-01T18:31:00Z","title":"EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.00863","snapshot_observed_at":"2026-08-07T13:04:30.647045Z","title":"Efficientsam: Leveraged masked image pretraining for efficient segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:30.647045Z"},"links":{"cited_paper":"/paper/2312.00863","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:5fe29cdf682eb90b24c60dde624905188a3497ec65cd4640b77bad97fa309256","observation_id":"a8540285-5f7d-4a9d-aa91-17cdcaba8555","resolution":{"observed_at":"2026-08-07T13:04:30.647045Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-10T01:12:16.468283Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-07T13:04:30.845275Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:30.845275Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:4bebfdb9a6e63ebec2350cec5e967a96b354bed95025482e307267740113b5a1","observation_id":"c7ddffe5-0cbf-48ad-8987-58c1f4ae77f0","resolution":{"observed_at":"2026-08-07T13:04:30.845275Z","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":"2026-08-07T13:04:30.969699Z","title":"Plop: Learning without forgetting for continual semantic segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:30.969699Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:776968f45867d939802ec0e6c7771ff2a0aa385bc2bcc9820b62b1c92c448117","observation_id":"823af435-6111-49ab-9316-6ac4174618dc","resolution":{"observed_at":"2026-08-07T13:04:30.969699Z","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":"2026-08-07T13:04:31.075718Z","title":"Modeling the background for incremental learning in semantic segmentation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:31.075718Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:a7bdbd7265bc43d93dff287d2853851b729f406bfda45b9aafc8f9c215f41653","observation_id":"4a7506a3-8fcb-4511-8911-7c2c82f458d1","resolution":{"observed_at":"2026-08-07T13:04:31.075718Z","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":"2026-08-07T13:04:32.657910Z","title":"Incrementer: Transformer for class-incremental semantic segmentation with knowl- edge distillation focusing on old class,","venue":null,"work_id":"d2d684e4-5903-4c82-9baa-27c2fbc205c3","year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:31.164967Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:d8221636f1a790efd3a6dfc131afcbd41af9300510f9cf6cf7c6f7b3a126a6dd","observation_id":"fac8017a-226e-4805-bff0-173fe5d62e16","resolution":{"observed_at":"2026-08-07T13:04:32.725010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:31.302248Z","title":"Comformer: Continual learn- ing in semantic and panoptic segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:31.302248Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:a1580bcd1d3fe703370910a8c00772a2b00c34f7d46169cb20be513ea1ee7db6","observation_id":"c84cb10a-2c85-4366-9e65-624779c3c14f","resolution":{"observed_at":"2026-08-07T13:04:31.302248Z","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":"2026-08-07T13:04:32.511474Z","title":"Incremental learning techniques for semantic segmentation,","venue":null,"work_id":"543a7859-8b0f-48ab-90dc-5a96e2ef3261","year":2019},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:31.396904Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:beef58ff4570b47fe73743aaef558325ad02661ee5fad23c90ac541e21e2447e","observation_id":"5aa49c2b-83c6-4135-9c25-7d4353c5b414","resolution":{"observed_at":"2026-08-07T13:04:32.599071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:31.508506Z","title":"Class similarity weighted knowledge distillation for continual semantic seg- mentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:31.508506Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:75c0d5bb751265bbbe39a7441f46e8b2b6c7ea15ec397fb81763ae12190ec7ad","observation_id":"3b736072-e5c7-4ffd-afd0-0578af3d865b","resolution":{"observed_at":"2026-08-07T13:04:31.508506Z","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":"2026-08-07T13:04:32.399234Z","title":"Towards a general-purpose foundation model for computational pathology,","venue":null,"work_id":"3a9c3a07-e33d-4c1a-a70c-273dd0d0a9f4","year":2024},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:31.613219Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:32540e2f437cde1c65c893fa7d947cee285cc55d8b924ec72b06b4eda0894342","observation_id":"d07c2298-6841-4ab9-ae78-3ddfef932f14","resolution":{"observed_at":"2026-08-07T13:04:32.440456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T13:04:32.209371Z","title":"A whole-slide foundation model for digital pathology from real-world data,","venue":null,"work_id":"6ad8208a-fa7d-4cab-8256-634675d6b4a6","year":2024},"citing_paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T13:04:31.782629Z"},"links":{"citing_paper":"/paper/2505.22855"},"observation_digest":"sha256:3893edf540f245b3393f4e277c5331fa7b6b0f9f04226493475dba0d6a65bd97","observation_id":"98d247e0-6640-49b4-ab91-19b7515d3a75","resolution":{"observed_at":"2026-08-07T13:04:32.312313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.22855","last_updated":"2025-05-28T20:41:56Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-10T00:29:37.077304Z","submitted_at":"2025-05-28T20:41:56Z","title":"IRS: Incremental Relationship-guided Segmentation for Digital Pathology"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":43},"total_outbound_references":59},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2505.22855."}