{"as_of":"2026-08-08T17:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53785ddc81a1d6cd31e5718b1c4017727d045f08906b005928255eeac48e3052","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T18:41:45.640290Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2502.10294/citation-record","integrity":"/paper/2502.10294/integrity","json":"/paper/2502.10294/citation-record.json","paper":"/paper/2502.10294"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T18:41:45.402303Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.402303Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:1d55091f443d96e1a817991c37f20877d121fed2d7ae1ff2487a53f2cb157477","observation_id":"9fb94a30-9d95-4146-9758-a09292d24efa","resolution":{"observed_at":"2026-08-07T18:41:45.402303Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03999","last_updated":"2018-05-20T23:33:30Z","snapshot_observed_at":"2026-07-06T06:32:53.966022Z","submitted_at":"2018-04-11T14:13:03Z","title":"Attention U-Net: Learning Where to Look for the Pancreas","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.03999","snapshot_observed_at":"2026-08-07T18:41:45.407991Z","title":"Attention u-net: Learning where to look for the pancreas.arXiv preprint arXiv:1804.03999, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.407991Z"},"links":{"cited_paper":"/paper/1804.03999","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:936e0fdaac59764c73aa5ba4e58dba4163f4cea1a484ee65ce524c60e7af9c69","observation_id":"731110d4-10ae-4e33-a268-ebc48ad52330","resolution":{"observed_at":"2026-08-07T18:41:45.407991Z","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":"2026-08-07T18:41:46.428557Z","title":"Unet++: A nested u-net architecture for medical imagesegmentation","venue":null,"work_id":"336c3b5b-f03c-4eff-a810-8a9d3426caa6","year":2018},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.412707Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:7fc99ca53a9428972da40db20d780ffda5970917b9ef9dd6538f02ffe69fa9d8","observation_id":"ff6cdc54-dd57-4703-8c6b-5832a92e805e","resolution":{"observed_at":"2026-08-07T18:41:46.432787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.10486","last_updated":"2018-09-27T12:25:52Z","snapshot_observed_at":"2026-07-06T07:04:39.537654Z","submitted_at":"2018-09-27T12:25:52Z","title":"nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.10486","snapshot_observed_at":"2026-08-07T18:41:45.418020Z","title":"nnu-net: Self-adapting framework for u-net-based medical image segmentation.arXiv preprint arXiv:1809.10486, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.418020Z"},"links":{"cited_paper":"/paper/1809.10486","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:7b764533ae62c832f7e0ee835db6e0ca5f53ef2f3916d994e31d9cb53c67027f","observation_id":"bb9daa42-983d-47b4-a90d-bbb1788e5431","resolution":{"observed_at":"2026-08-07T18:41:45.418020Z","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":"2026-08-07T18:41:46.413724Z","title":"Rt-unet: an advanced network based on residual network and transformer for medical image segmentation.International Journal of Intelligent Systems, 37(11):8565–8582, 2022","venue":null,"work_id":"c40536d5-e800-4268-9f61-a0564f4ba05e","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.423467Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:d67e57a593ca7932ae7d95085679eb13172e8e8993da2a8b8a73aae64c11918e","observation_id":"3409e5ca-a975-4060-9d67-257fa216ebd9","resolution":{"observed_at":"2026-08-07T18:41:46.418603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.399126Z","title":"Transcunet: Unet cross fused transformer for medical image segmentation.Computers in Biology and Medicine, 150:106207, 2022","venue":null,"work_id":"79f57a5c-f07d-40d3-90d1-cea115aef7b8","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.428206Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:3a1c6548ecb4c376f4681ee3f4709e0df47366a1ea406452a44f35acf1ad8669","observation_id":"3648f761-ab2c-4a02-b820-f0d880800db1","resolution":{"observed_at":"2026-08-07T18:41:46.403620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.382941Z","title":"Nguyen-Tat, Thien-Qua T","venue":null,"work_id":"26a0594a-25d3-402a-bf1b-8f2680fc856f","year":2024},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.433199Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:ae5fdac76ab4b34639521820278fe8559ef4fc3574904c9cd1f1483a49849c37","observation_id":"5e00a9aa-3061-4d5c-8781-0f658bfa00a0","resolution":{"observed_at":"2026-08-07T18:41:46.388005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2026-08-07T18:41:45.437877Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale.arXiv preprint arXiv:2010.11929, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.437877Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:ff3d0d7d73beccb710850ab401c335b309d2c970f3c68f31e659cbd32fd5d66a","observation_id":"199544f9-6645-4e21-99da-4a21e49ea4f2","resolution":{"observed_at":"2026-08-07T18:41:45.437877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04306","last_updated":"2021-02-08T16:10:50Z","snapshot_observed_at":"2026-07-06T10:39:29.945712Z","submitted_at":"2021-02-08T16:10:50Z","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04306","snapshot_observed_at":"2026-08-07T18:41:45.442577Z","title":"Transunet:Transformers make strong encoders for medical image segmentation.arXiv preprint arXiv:2102.04306, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.442577Z"},"links":{"cited_paper":"/paper/2102.04306","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:ba6b8fc09f4be7b8ae6b70d8eb39f6d0a48ef5a6773482f63ed0f749147735e8","observation_id":"aa1efbbb-bc52-456c-b0f8-873786e492d3","resolution":{"observed_at":"2026-08-07T18:41:45.442577Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.03201","last_updated":"2022-02-04T06:53:37Z","snapshot_observed_at":"2026-08-04T08:02:54.298886Z","submitted_at":"2021-09-07T17:08:24Z","title":"nnFormer: Interleaved Transformer for Volumetric Segmentation","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.03201","snapshot_observed_at":"2026-08-07T18:41:45.446992Z","title":"nnformer:Interleavedtransformerforvolumetric segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.446992Z"},"links":{"cited_paper":"/paper/2109.03201","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:85b83222753d346eec302d217ba48aa21fae4617b754a1e44b889549873bf200","observation_id":"20752a50-7f8b-4fe1-9954-a4a0a4f71003","resolution":{"observed_at":"2026-08-07T18:41:45.446992Z","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":"2026-08-07T18:41:45.451525Z","title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.451525Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:0d5c7f6f39aadd9506705ffb6cb6a2e86be07a4937f1229305ebfadec53260a6","observation_id":"f392d120-38cc-45d0-a112-cabaf9c85ffc","resolution":{"observed_at":"2026-08-07T18:41:45.451525Z","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":"2026-08-07T18:41:46.358382Z","title":"Unetr: Transformers for 3d medical image segmentation","venue":null,"work_id":"b172fb80-7713-4ebf-8b1c-3879f375c806","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.455796Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:382316324e953975bff708f8fb591c59a9937b2bbcd35ea678dc9df756453d4f","observation_id":"37b3f060-aefc-4fb2-9213-92a13b1f6f0a","resolution":{"observed_at":"2026-08-07T18:41:46.363005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.343965Z","title":"A robust volumetric transformer for accurate 3d tumor segmentation","venue":null,"work_id":"5efccdac-2f5a-4aca-a1b7-3a2c7ba22785","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.459755Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:aa424b0bd326c7284915a104fbbf8d87661b2a52ab1e4f61766b3f10e5008e5b","observation_id":"6288241f-e745-4caf-8989-9d17c3c93526","resolution":{"observed_at":"2026-08-07T18:41:46.348422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.329693Z","title":"Levit-unet:Makefasterencoderswithtransformerformedicalimagesegmentation","venue":null,"work_id":"53fbaf86-0eb3-4f6e-8f37-8bb2490063c3","year":2023},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.463531Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:e625b413f80f4ca893dfba6033f30724c8692c13d22de634a4f70fc7205b8130","observation_id":"6deee9fe-0a4e-4a50-af0a-ef6d6a2e0975","resolution":{"observed_at":"2026-08-07T18:41:46.334391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.06716","last_updated":"2023-04-13T17:59:13Z","snapshot_observed_at":"2026-07-06T15:15:26.125395Z","submitted_at":"2023-04-13T17:59:13Z","title":"STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.06716","snapshot_observed_at":"2026-08-07T18:41:45.468086Z","title":"Stu- net: Scalable and transferable medical image segmentation models empowered by large-scale supervised pre-training.arXiv preprint arXiv:2304.06716, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.468086Z"},"links":{"cited_paper":"/paper/2304.06716","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:65367683f66c3c7e59d4587131a7efabe0578d7d4f4d1928dfea68d7d5912f75","observation_id":"23777f92-369e-46bc-a986-921844b1e557","resolution":{"observed_at":"2026-08-07T18:41:45.468086Z","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":"2026-08-07T18:41:46.315005Z","title":"Scribble-based hierarchical weakly supervised learning for brain tumor segmentation","venue":null,"work_id":"c6339df6-5ce9-45e2-9f5f-567dd97dd39d","year":2019},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.472884Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:21857d839c8c89975ffa2154681fda41112046557e270137c4813048ec7408ca","observation_id":"1eac245f-cbdd-448a-b766-2a6751330bc8","resolution":{"observed_at":"2026-08-07T18:41:46.319662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.300258Z","title":"Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision","venue":null,"work_id":"c6686c37-d4fa-487b-a32c-729a12221bd5","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.477242Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:2cd986d434d0b11bec4fbfaa121d1a062001810e322c9b1fb55c7f35d7807bdb","observation_id":"5578af26-0652-4e40-93fe-fdda05727e9e","resolution":{"observed_at":"2026-08-07T18:41:46.305256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.284303Z","title":"Scribblevc: Scribble-supervised medical image segmentation with vision-class embedding","venue":null,"work_id":"f21cf078-06b5-4d58-acf8-ce7b6d9ca34a","year":2023},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.481465Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:793e990e22e6c6aa43baacc4aedf3270938253e2b6221700a5965de0f7c6dbcb","observation_id":"09a0fb5b-3c33-4078-8424-7b6946febde2","resolution":{"observed_at":"2026-08-07T18:41:46.289900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.267681Z","title":"Scribformer: Transformer makes cnn work better for scribble-based medical image segmentation.IEEE Transactions on Medical Imaging, 2024","venue":null,"work_id":"0b645903-712b-494d-8b6e-7184c4f3c9af","year":2024},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.485982Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:a9df1eeb94e1b5d1437019de3dbdccb6e4b9cfa84b3b1785b0fbb52b1157d6df","observation_id":"ab08a517-9230-4e03-baa6-d286a3af8db8","resolution":{"observed_at":"2026-08-07T18:41:46.272600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.252180Z","title":"Weakly-supervisedsalientobjectdetectionviascribbleannotations","venue":null,"work_id":"94dcaeb9-8521-40a8-a59e-f4faf0579a9c","year":2020},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.490478Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:09b713832dbffd147d37c5e7e80862a5a6b9ae98e616725c126af9290cdafe61","observation_id":"28843eac-64d5-407a-a96e-842a649ed4cd","resolution":{"observed_at":"2026-08-07T18:41:46.257056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.236004Z","title":"Cyclemix: A holistic strategy for medical image segmentation from scribble supervision","venue":null,"work_id":"a10d6d0d-7593-44c9-8cf9-ed4e2126bdfa","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.494574Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:449eec4c9f0ffab5104235f7e5f20496b70dbd45c52990d976e8ff786d66001c","observation_id":"6021e589-abca-4a44-b605-40c764f320d8","resolution":{"observed_at":"2026-08-07T18:41:46.241335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.221485Z","title":"S 2 me: Spatial-spectral mutual teaching and ensemble learning for scribble-supervised polyp segmentation","venue":null,"work_id":"12b240fc-3402-4810-b720-93600fd86986","year":2023},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.498717Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:5ff982e3bed1e391e27bbcac3149dad5e648b9e94994dafe85e89919cab85434","observation_id":"778662c4-27e8-42ab-903b-9892223acf08","resolution":{"observed_at":"2026-08-07T18:41:46.226165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.206434Z","title":"In European conference on computer vision, pages 459–479","venue":null,"work_id":"083f4aeb-d174-401a-9aa0-55255b2eb9b1","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.502925Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:dba79099fe16991893287d1f3788eb90c43bda1e1aaa7edd4076d664b38a5b24","observation_id":"2896beb1-1536-43c4-8deb-88fc4d827c17","resolution":{"observed_at":"2026-08-07T18:41:46.211466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.507182Z","title":"Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?IEEE transactions on medical imaging, 37(11):2514–2525, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.507182Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:910a28b251782904a37fa558e1de35a08b453a16e468c764ae7ee703f17aa31c","observation_id":"5a65de7a-60ab-42b8-ade9-dbf51e0c9275","resolution":{"observed_at":"2026-08-07T18:41:45.507182Z","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":"2026-08-07T18:41:46.180885Z","title":"Learning to segment from scribbles using multi-scale adversarial attention gates","venue":null,"work_id":"f7f8dadc-6d83-4220-a84d-f3edb32e7942","year":1990},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.511302Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:56bd1cdf048274028c48da7c07bb7b6c0b730364384154a92c1b18d078b29c73","observation_id":"bd1e7627-1085-490a-a298-5eb9c2d45a5d","resolution":{"observed_at":"2026-08-07T18:41:46.186162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.164110Z","title":"Multivariatemixturemodelforcardiacsegmentationfrommulti-sequencemri","venue":null,"work_id":"bc823275-9723-40cc-ab05-9ec2b0771f8d","year":2016},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.515540Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:60e7a51acc627cffe66380fa9b649c42c903fb85526a0e7e5535e4fd4833a9fa","observation_id":"3e20839d-2181-4a6c-9edb-960065fde078","resolution":{"observed_at":"2026-08-07T18:41:46.168575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.148390Z","title":"Multivariate mixture model for myocardial segmentation combining multi-source images.IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12):2933–2946, 2019","venue":null,"work_id":"e616d98a-5da4-490f-b74a-c99faca6b573","year":2019},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.520180Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:ae9bec085b4bc79baa5ce332e8897b06fe91343e85117eb08d40cadc0e7325ba","observation_id":"f0bd1101-d852-4bf2-8a8b-f3435a2e026f","resolution":{"observed_at":"2026-08-07T18:41:46.153111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.132192Z","title":"Shapepu: A new pu learning framework regularized by global consistency for scribble supervised cardiac segmentation","venue":null,"work_id":"41ae5658-000a-4904-9471-1bf29e94aaba","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.524463Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:5a43e80fde067c9658dc3347345f3c7e9023241956be3863c7d0c7b35dde0775","observation_id":"ec05e760-c3ff-4c2f-b38d-5d5c00aaa41d","resolution":{"observed_at":"2026-08-07T18:41:46.137298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.116400Z","title":"Video polyp segmentation: A deep learning perspective.Machine Intelligence Research, 19(6):531–549, 2022","venue":null,"work_id":"40021dc1-bc0a-4b15-a17b-5c80132e979d","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.528661Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:efb8212b94b78bf90f425cc9f571f6a0a5b456acf6b389082a1d2f578dd4bb15","observation_id":"28989abd-d12f-4fd9-a3c9-2a9938e4705c","resolution":{"observed_at":"2026-08-07T18:41:46.121909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.101429Z","title":"Progressively normalized self-attention networkforvideopolypsegmentation","venue":null,"work_id":"f49a1c13-1847-421a-af78-fb12ec58622f","year":2021},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.533095Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:bb1bf66214e5bd826e99393d5a2d00f246b6994129f2dc505aaede5451af3fe7","observation_id":"cd3f3e63-bd98-49a1-84cc-a0757e54e283","resolution":{"observed_at":"2026-08-07T18:41:46.106138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.537764Z","title":"Pranet: Parallel reverse attention network for polyp segmentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.537764Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:7584a74bc5fb191e15d703cf7189239193f6204d7482bc8ee5f168a3143803ff","observation_id":"256cbe0d-f2b2-44b1-8d22-791e992ff3a6","resolution":{"observed_at":"2026-08-07T18:41:45.537764Z","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":"2026-08-07T18:41:46.076689Z","title":null,"venue":null,"work_id":"8d58211a-e18a-4e2d-85b9-fedca2a3f6c6","year":2021},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.541822Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:4c43cfa2996a6634531374c16f5ebd7911566bffbe91363dfa42b19fd6afa8d4","observation_id":"3d300f76-b847-41ee-a251-6268051d023d","resolution":{"observed_at":"2026-08-07T18:41:46.081296Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.061272Z","title":"Dataset of breast ultrasound images.Data in brief, 28:104863, 2020","venue":null,"work_id":"34c06478-d637-4bcf-b9f5-3f4ce482b455","year":2020},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.545715Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:ac0f3435f573a96f624775bce1382aebfd54f74628313757b215990282edbe77","observation_id":"00f11c44-67b5-43cd-804a-419ea4a1bde9","resolution":{"observed_at":"2026-08-07T18:41:46.066374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:46.045868Z","title":"The treasure beneath multiple annotations: An uncertainty-aware edge detector","venue":null,"work_id":"18a2f5f6-c6c8-4903-bb62-891cb99d160b","year":2023},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.550091Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:b2338f1dece118cc7abc980a375eb2b9e699eb72bca1beeac4e98f8e36a2cb2d","observation_id":"e1bbeb0d-e1e2-4fd0-ad76-921b7309c6d5","resolution":{"observed_at":"2026-08-07T18:41:46.050869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.554134Z","title":"Masked-attention mask transformer for universal image segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.554134Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:ac15cbac307bf09eb68f6fa956a0c167f418326f492cc458e0641e527156eb51","observation_id":"a3799e22-67a3-44fd-a6f0-495657c62a9b","resolution":{"observed_at":"2026-08-07T18:41:45.554134Z","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":"2026-08-07T18:41:46.022182Z","title":"Mask2former with improved query for semantic segmentation in remote-sensing images.Mathematics, 12(5):765, 2024","venue":null,"work_id":"14579f6a-6608-4011-9b5f-fc7953d518fc","year":2024},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.558769Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:30900b6cfea284a627405c2c216b571ce844081a17ec628135ceda56c9c946bb","observation_id":"fc36b10b-121d-401b-b554-1bccbee65542","resolution":{"observed_at":"2026-08-07T18:41:46.027176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08396","last_updated":"2024-03-29T12:50:38Z","snapshot_observed_at":"2026-07-06T15:27:09.914635Z","submitted_at":"2023-05-15T07:23:54Z","title":"MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.08396","snapshot_observed_at":"2026-08-07T18:41:45.563144Z","title":"Maxvit-unet: Multi-axis attention for medical image segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.563144Z"},"links":{"cited_paper":"/paper/2305.08396","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:fa79cda6ad0f8b83173a1b6c5b542f54eaacb31a6ba59711da4b2ec083d5708e","observation_id":"b67a5e65-4a91-4e51-b7cf-55db0867c36e","resolution":{"observed_at":"2026-08-07T18:41:45.563144Z","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":"2026-08-07T18:41:45.567680Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.567680Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:2ac762831521ca69ec19eda91d10bb2029ba299f6b9f650a4a26d13d488fc076","observation_id":"720f4b0e-524f-4e6c-a492-86052b66bb7d","resolution":{"observed_at":"2026-08-07T18:41:45.567680Z","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":"2026-08-07T18:41:45.997577Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","venue":null,"work_id":"6cd7094e-0e5f-4379-bb80-d4828b0ce29a","year":2015},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.572038Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:df59319d4f456f496945282be3c5edf273919adc2fcfe5ed44c8810ce2e51b53","observation_id":"f4268945-50fe-4d0f-abe5-b34076aa9b88","resolution":{"observed_at":"2026-08-07T18:41:46.002359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.08375","last_updated":"2026-04-14T12:21:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-03-22T14:30:17Z","title":"Deep Learning using Rectified Linear Units (ReLU)","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.08375","snapshot_observed_at":"2026-08-07T18:41:45.576208Z","title":"Deep learning using rectified linear units (relu).arXiv preprint arXiv:1803.08375, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.576208Z"},"links":{"cited_paper":"/paper/1803.08375","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:3d7035d506c1d2c4c6af73df724c9a569c72dc58a0d2c6de2877f72f1a6178c1","observation_id":"9298f4ee-a233-4507-bd92-0ee1ccb31640","resolution":{"observed_at":"2026-08-07T18:41:45.576208Z","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":"2026-08-07T18:41:45.980545Z","title":"Et-net: A generic edge-attention guidance network for medical image segmentation","venue":null,"work_id":"4342d1b1-2c7f-4f7c-b740-505e148ad890","year":2019},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.581050Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:52f5d48fb19ef3977f31e04f290d239c7d0c551bb9ea3f36447ad890c8c6a608","observation_id":"baf5f6da-2cb4-4ebd-b718-be18db2e2228","resolution":{"observed_at":"2026-08-07T18:41:45.986253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.963920Z","title":"Per-pixel classification is not all you need for semantic segmentation.Advances in neural information processing systems, 34:17864–17875, 2021","venue":null,"work_id":"2f318b6d-f120-4edb-8323-5d9c81be96c8","year":2021},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.585125Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:f6c4d055744cb8d335a888224ee906e38d877ce647f982488a1f78d4e28050d9","observation_id":"7652364f-1c55-4839-b500-371d4d4a7ca3","resolution":{"observed_at":"2026-08-07T18:41:45.969258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.948636Z","title":"Query-guided generalizable medical image segmentation.Pattern Recognition Letters, 184:52–58, 2024","venue":null,"work_id":"ea3fc454-1d3f-4917-9274-d95d64e5627b","year":2024},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.590844Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:af26907cef8524516bff6099cc13e0b4cad11b6b553487b205382c9184879f78","observation_id":"f2eea5cb-31c9-412f-b4f4-04adb38b4510","resolution":{"observed_at":"2026-08-07T18:41:45.954077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.933491Z","title":"Segment anything","venue":null,"work_id":"421e9fdc-e5f9-480a-8bf3-01b762a973e8","year":2023},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.595634Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:c36df216952407be893514d611160ed613b267e9345713a533b74b0921b8cb8d","observation_id":"c448f7dc-182d-4b57-bd18-3cd85aece2b8","resolution":{"observed_at":"2026-08-07T18:41:45.938442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.918374Z","title":"Sparse instance activation for real-time instance segmentation","venue":null,"work_id":"f3f5b951-c060-4042-8a6e-cdc7956e92ed","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.600332Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:28e0bbb240a6164ea7b9c9e3c52a3ddf340e2a874db59ae6966d792d7a1c3b20","observation_id":"fceeb8eb-d49c-4d92-83da-3b075da5cb30","resolution":{"observed_at":"2026-08-07T18:41:45.922930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.903132Z","title":"In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117–2125, 2017","venue":null,"work_id":"253b43b2-f4a3-41ca-9c2b-689776d0347e","year":2017},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.604747Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:33f4fad3243542c228be64fc7ef325c939f78142154f9e6af71a69827df2aa2f","observation_id":"9dfcba5b-941e-4bf3-81fc-3a217e59c971","resolution":{"observed_at":"2026-08-07T18:41:45.908037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.609129Z","title":"Pyramid scene parsing network","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.609129Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:b60b0de82606c9baa04cfe4932625632ca2c91840a32f945204b3fe59859b788","observation_id":"eaba26fe-900a-4dd9-86fb-66ee8d761d54","resolution":{"observed_at":"2026-08-07T18:41:45.609129Z","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":"2026-08-07T18:41:45.876940Z","title":"Puzzle mix: Exploiting saliency and local statistics for optimal mixup","venue":null,"work_id":"ed8d7f2e-d899-4595-9f12-0e493f556706","year":2020},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.613400Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:b958b81571d9de1f76297062abcefd78108986e7412722ff44c4c3c33191e2ec","observation_id":"ebeb5e54-11c5-4877-ae36-b53fdb493de6","resolution":{"observed_at":"2026-08-07T18:41:45.881521Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1708.04552","last_updated":"2017-11-29T14:51:40Z","snapshot_observed_at":"2026-07-06T05:55:22.966528Z","submitted_at":"2017-08-15T15:21:53Z","title":"Improved Regularization of Convolutional Neural Networks with Cutout","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.04552","snapshot_observed_at":"2026-08-07T18:41:45.617372Z","title":"Improved regularization of convolutional neural networks with cutout","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.617372Z"},"links":{"cited_paper":"/paper/1708.04552","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:be96da988a8cf205ad110abe57700ed273b15489e7746397bb5a938144291695","observation_id":"2ca5c142-c0b0-4c17-be2e-1475fb1edf1f","resolution":{"observed_at":"2026-08-07T18:41:45.617372Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-07T18:41:45.621898Z","title":"mixup: Beyond empirical risk minimization.arXiv preprint arXiv:1710.09412, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.621898Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:317ad6e4ecd10e0f210be14e7a64dd10dfe004b8ba864cd2af4b098b76c16a1a","observation_id":"88fd0921-83f1-49da-b17b-cc7a5dad3c49","resolution":{"observed_at":"2026-08-07T18:41:45.621898Z","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":"2026-08-07T18:41:45.861802Z","title":"Scribblesup:Scribble-supervisedconvolutionalnetworksforsemanticsegmentation","venue":null,"work_id":"8b3ae696-0d03-4fa9-92f7-d9da40fa87d7","year":2016},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.626786Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:10cb634856ed6f8a672cbb55c17ea2144a8ae720c3c9c2af44cbe5535cba8aea","observation_id":"517d6e1e-4067-49a4-adc4-dd50903e993c","resolution":{"observed_at":"2026-08-07T18:41:45.866596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.846770Z","title":"Semi-supervisedlearningbyentropyminimization","venue":null,"work_id":"5eca015d-d1d3-4a1f-9560-36c04f671258","year":2004},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.631454Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:dd37fdcca1e83954c3bd5fb18e8a42d5dc6976997016854edf3356da0b307e15","observation_id":"be001033-8db7-425d-88bf-a649dc13b696","resolution":{"observed_at":"2026-08-07T18:41:45.851409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.831747Z","title":"Weakly supervised segmentation of covid19 infection with scribble annotation on ct images.Pattern recognition, 122:108341, 2022","venue":null,"work_id":"5807fa92-19db-410d-94b6-e07e163c3968","year":2022},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.635957Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:cbba84429662190c8fda94e5a20cd42f2f5236af60767b9f0846df86041cff0d","observation_id":"92587bea-abe3-4ba5-bd40-972efaa498fb","resolution":{"observed_at":"2026-08-07T18:41:45.836753Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T18:41:45.814072Z","title":"Semi-supervised semantic segmentation with cross pseudo supervision","venue":null,"work_id":"7f417cf4-3c4e-4045-a181-0c93a1413723","year":2021},"citing_paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T18:41:45.640290Z"},"links":{"citing_paper":"/paper/2502.10294"},"observation_digest":"sha256:388857546c86c3b1555d61c7c3c6c8509ab53f84f503170d46ddeebab41d9005","observation_id":"62c35a76-0b78-4f7f-b298-c26955ed3f19","resolution":{"observed_at":"2026-08-07T18:41:45.820619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.10294","last_updated":"2025-02-14T16:56:24Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T18:36:03.334528Z","submitted_at":"2025-02-14T16:56:24Z","title":"QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":0,"verified_fuzzy":36},"total_outbound_references":54},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2502.10294."}