{"as_of":"2026-08-19T23:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:44d40881137ef2eaaac2334f5d781e8cf3ad674106825effc9ebc80f97038186","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T19:18:02.715681Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-15T04:56:33.261444Z","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":[{"citation":{"cited_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.05473","snapshot_observed_at":"2026-07-15T04:56:33.261444Z","title":"LMS-Net: a learned Mumford-Shah network for few-shot medical image segmentation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.12586","last_updated":"2026-07-14T09:56:03Z","snapshot_observed_at":"2026-08-19T17:45:06.778209Z","submitted_at":"2026-07-14T09:56:03Z","title":"Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-15T04:56:33.261444Z"},"links":{"cited_paper":"/paper/2502.05473","citing_paper":"/paper/2607.12586"},"observation_digest":"sha256:1b738f7166c7b2700cf83cd36c6b5714fb9fbb28d95905df0bd8a09dd3da9b9a","observation_id":"c99bea82-1a0c-454c-a30e-32ea18d08591","resolution":{"observed_at":"2026-07-15T04:56:33.261444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2502.05473/citation-record","integrity":"/paper/2502.05473/integrity","json":"/paper/2502.05473/citation-record.json","paper":"/paper/2502.05473"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T19:18:03.498317Z","title":"A survey on medical image segmentation,","venue":null,"work_id":"00c6e4f5-a340-45f1-8822-b9a1f67dbfc6","year":2015},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.515355Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:71c0553418506c1f464dbfba5c6797162bb641c5a173bba24725e7366c09ae71","observation_id":"3342dcaf-efff-4d9c-8283-9e31af703e29","resolution":{"observed_at":"2026-08-08T19:18:03.502868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.482330Z","title":"A deep learning-based auto-segmentation system for organs-at-risk on whole-body computed tomography images for radiation therapy,","venue":null,"work_id":"ae078e2a-4fdb-40c2-a741-1c46f7b1d99f","year":2021},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.520821Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:671bb22ded8171b17252edab0775ea03f0d7aabbdc8cb9832a4850bfb1b11b65","observation_id":"65ec0add-2da3-47ce-a917-86eb352adb84","resolution":{"observed_at":"2026-08-08T19:18:03.487323Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.467116Z","title":"Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning a,","venue":null,"work_id":"d11fb320-da8d-40f4-a85d-e8d895eadc49","year":2007},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.526879Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:7e6b67df18288e151c219085f4c35f125a1ad52e659ca324fe79cc4c48c24573","observation_id":"09a26cf7-c99e-488a-99d9-8c3732631e85","resolution":{"observed_at":"2026-08-08T19:18:03.472216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.451110Z","title":"Segnet: A deep con- volutional encoder-decoder architecture for image segmentation,","venue":null,"work_id":"72cac9fa-1fd6-451d-83b5-054bb7ec5d92","year":2017},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.531605Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:8b3325979d49aa41fbece70cf483f46ef962a6204b50fb233070b973c2745870","observation_id":"34743d81-2066-4aa8-8d3b-374a4f0aa209","resolution":{"observed_at":"2026-08-08T19:18:03.456589Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04306","last_updated":"2021-02-08T16:10:50Z","snapshot_observed_at":"2026-08-19T08:02:16.487223Z","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":"2026-08-08T19:18:02.536373Z","title":"Transunet: Transformers make strong encoders for medical image segmentation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.536373Z"},"links":{"cited_paper":"/paper/2102.04306","citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:6c28608acbe2090d0d7095676071479a00290c49ab155587e959881ff0fb5c31","observation_id":"cda7c435-30a9-490a-81a1-4194efde87fe","resolution":{"observed_at":"2026-08-08T19:18:02.536373Z","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":"2026-08-08T19:18:03.434784Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":"ec090c55-2dce-4731-8165-4476160c8825","year":2015},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.541982Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:b090a0fa6402fff0310a2e2bf33def234ee70b150fbb977edb1e39d9aa58458d","observation_id":"73951443-8259-4890-87d8-4b4f3bfc1544","resolution":{"observed_at":"2026-08-08T19:18:03.439638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.406782Z","title":"Active contours without edges,","venue":null,"work_id":"8a89b10b-1251-4aac-8e77-e4e72a5f098a","year":2001},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.552315Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:a520ff9dcf93f67023741b37877de360d32e6cee3a1d04b6982fdd89c355cb31","observation_id":"534b8b51-56e9-41c9-b6f1-f21057588eae","resolution":{"observed_at":"2026-08-08T19:18:03.411917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.393221Z","title":"A multiphase level set framework for image segmentation using the mumford and shah model,","venue":null,"work_id":"0e98ee4d-12c6-4d6e-9d2e-3f4cbfa6cb9b","year":2002},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.557341Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:bb31be42c05357025411e2227088978f082d3c8b798de3b6677f0802a96a0a14","observation_id":"801285b8-7d82-46fc-a605-da3ed7269e66","resolution":{"observed_at":"2026-08-08T19:18:03.397331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.379536Z","title":"Panet: Few-shot image semantic segmentation with prototype alignment,","venue":null,"work_id":"2bcb1459-26f8-43fb-b505-7913d611673a","year":2019},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.562035Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:f5dac6e1344cebfd652b868e984ee766ead36cd1b9dd0fa7f457991abe1af46a","observation_id":"ba2c7c79-be32-4811-8df0-e8cc3b85ccbe","resolution":{"observed_at":"2026-08-08T19:18:03.384135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.363093Z","title":"Flexisp: A flexible camera image processing framework,","venue":null,"work_id":"d66fa8e3-f4fb-43bc-b644-c73658f70595","year":2014},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.566609Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:c5d42eefaa7f2b06dd54ac726829f483d402dbbb346bec6e1f2ae9dd41a3f26a","observation_id":"1f422a87-0ae9-4b73-997a-2c9f1b7be096","resolution":{"observed_at":"2026-08-08T19:18:03.368587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.348276Z","title":"Learned primal-dual reconstruction,","venue":null,"work_id":"a31d5b8e-0490-49d0-8fd9-8f0f7cb3c60e","year":2018},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.571289Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:1130b8206d477fad01e13244b74a375948b2524186409d60ea108df86363f25f","observation_id":"ecbb973c-459c-4360-86a2-c8f3f7e9d984","resolution":{"observed_at":"2026-08-08T19:18:03.353012Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.331568Z","title":"A first-order primal-dual algorithm for convex problems with applications to imaging,","venue":null,"work_id":"fd308f48-ff18-4484-bc3b-352da90bc404","year":2011},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.576071Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:a03613c2b09044b50de3e85f8eab3e103e8ce6e173197437aedd171093d0f8f6","observation_id":"97a69908-0911-4fcf-9b7d-998114d6cdc6","resolution":{"observed_at":"2026-08-08T19:18:03.337986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.314974Z","title":"Optimal approximations by piecewise smooth functions and associated variational problems,","venue":null,"work_id":"60975abd-116f-436e-89fe-f743ad0797c1","year":1989},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.580913Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:eaff15a463dd0bfeff0d99b0c4aca78d0218eac21a3262f6077dd0e76c9fd925","observation_id":"9ef367a1-6c79-4af7-bc09-21acb4988a85","resolution":{"observed_at":"2026-08-08T19:18:03.320014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.298622Z","title":"Ista-net: Interpretable optimization-inspired deep network for image compressive sensing,","venue":null,"work_id":"15198939-c7dd-40df-b8eb-c92f4c06bf7c","year":2018},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.585570Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:48c1d275431b43935fd3839efad3f421795ec2d035aa5d696fdcf4ffaef290ba","observation_id":"c1ca63c6-8bd5-4d31-8c60-2426eb54dbcf","resolution":{"observed_at":"2026-08-08T19:18:03.304014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.283146Z","title":"Admm-csnet: A deep learning approach for image compressive sensing,","venue":null,"work_id":"bf8b4903-82a4-4811-97e5-5560db099812","year":2018},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.590152Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:1ce8a2d4fbc4ae0235def6d8ecb502754b56c5990c1ccd6ac409ab12b0fc047f","observation_id":"85aff2a1-2de5-4551-a38a-a800f66aeb46","resolution":{"observed_at":"2026-08-08T19:18:03.287857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.266912Z","title":"Nonlocal regularized cnn for image segmentation,","venue":null,"work_id":"4973bc0d-0379-4031-96bc-7a2fabc776eb","year":2020},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.594749Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:be4095f8d963381f333cf4a6a24423f9fce65734106e478413aa60000e726727","observation_id":"51c89633-a236-49e6-bc21-388a83b8958f","resolution":{"observed_at":"2026-08-08T19:18:03.272336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.249175Z","title":"Deep convolutional neural networks with spatial regularization, volume and star-shape priors for image segmentation,","venue":null,"work_id":"e46ade7b-f9a6-4d44-8c1f-1bcf528f8593","year":2022},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.599538Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:b610164338489ced85f742ce9b8b9a9bf49a20cbe6b0b07047af5c05f0f8f6e9","observation_id":"3e43a2a0-1cdb-4a6a-b3b8-d991cbb87185","resolution":{"observed_at":"2026-08-08T19:18:03.254411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.547393Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.547393Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:5856fccfeb7e0f2240ad2475a4d447815905b69ea34fb3a88b2de8497cdc35fc","observation_id":"2db1333d-c564-4f72-92df-9946079f1fd1","resolution":{"observed_at":"2026-08-08T19:18:02.547393Z","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":"2026-08-08T19:18:03.232705Z","title":"Assembling a learnable mumford–shah type model with multigrid technique for image segmen- tation,","venue":null,"work_id":"879cb625-8949-471a-aea3-dcb9da87c0ca","year":2024},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.604383Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:35d94be3092bf80390f985eff3edcc3e56dbe6a68180e2ed0161f9873f9f9b4e","observation_id":"58f2539a-c73a-4cd9-8962-58991d06420d","resolution":{"observed_at":"2026-08-08T19:18:03.238110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.215550Z","title":"Global minimization for continuous multiphase partitioning problems using a dual approach,","venue":null,"work_id":"eeae2058-ad08-4985-a5a4-a41fefb6347a","year":2011},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.608905Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:5d674f3bac20d3c4afab8727857145fc1f63e75bbfb5df9e1b3b07b3f7409dd7","observation_id":"13fabf41-d2f8-4ab2-9d45-cc0104d3835d","resolution":{"observed_at":"2026-08-08T19:18:03.220992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.199007Z","title":"Denoising prior driven deep neural network for image restoration,","venue":null,"work_id":"1f6d2e3d-3805-4368-94a1-6e42887bdfbc","year":2018},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.613495Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:8a7516ca3de3bd436b1be3aa1d904e4b818f75f2284a242e71da53c8d5db735d","observation_id":"a6dd1ff4-6a90-4e60-98fa-bf40ab8ef714","resolution":{"observed_at":"2026-08-08T19:18:03.204531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.182441Z","title":"Deep unfolding network for image super-resolution,","venue":null,"work_id":"8c3312ac-62a6-47ed-9459-5c96bd15c48c","year":2020},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.618349Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:0bbbe1af5cb7a9574f67f48e1d2bd32fbf67848d32de4aece989c48229ff4085","observation_id":"65ba9d8f-a7dc-4b0b-b585-70609f325866","resolution":{"observed_at":"2026-08-08T19:18:03.187732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.622823Z","title":"Cpp-net: Embracing multi-scale feature fusion into deep unfolding cp-ppa network for compressive sensing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.622823Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:146440369389417ff5f825e890d654e66c55265f397e3c95e526ca425466d19b","observation_id":"e362e34f-a98f-4d51-b530-9a42fe9ab3e0","resolution":{"observed_at":"2026-08-08T19:18:02.622823Z","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":"2026-08-08T19:18:03.136628Z","title":"A multiphase image segmentation method based on fuzzy region competition,","venue":null,"work_id":"ce35f9e8-0358-46bc-8560-f181b3279204","year":2010},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.627432Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:f23563735544f4639d18dd9c24e09305a739a2bda0bfa726c4e3196ffe298a4d","observation_id":"92a06311-d6fa-4e0e-a9b5-82fe6d8ef141","resolution":{"observed_at":"2026-08-08T19:18:03.159287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.061208Z","title":"Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,","venue":null,"work_id":"b2f4237b-8f49-4ce7-976c-f0a068ef8607","year":2016},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.632689Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:8c64e5a52020d50fca7e7345cf876dffb0da8dc56a55df728f5c52b6ae42a771","observation_id":"634a5628-5759-4725-956f-2b591a92da96","resolution":{"observed_at":"2026-08-08T19:18:03.088199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.034143Z","title":"Unfolded proximal neural networks for robust image gaussian denoising,","venue":null,"work_id":"f1a3cada-9bdd-4ea3-9f6f-36efcce25c23","year":2024},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.637473Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:72db163f7055ea012ea877171965705ab8fb1293a063eaeaab1ff2afe50ab76a","observation_id":"5048f72f-91e5-4019-841b-2897020b625c","resolution":{"observed_at":"2026-08-08T19:18:03.040525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:03.012234Z","title":"Some generalized order-disorder transformations,","venue":null,"work_id":"1859a941-6918-49d6-b491-13dd3457f1c4","year":1952},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.642023Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:2aff9bc9b030306b401121782905cfbf504af40d70635a5280075a172bbcae09","observation_id":"6e060b55-2e1f-4178-91ce-78b28cbedd98","resolution":{"observed_at":"2026-08-08T19:18:03.018882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.989176Z","title":"Signal recovery by proximal forward- backward splitting,","venue":null,"work_id":"cc736df1-f17c-43b8-84ab-b4c68d0dbf86","year":2005},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.646919Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:cac7f1b7fa0acd2ad1cba6b434048ff584ce363b10533a21189c4ae812147f78","observation_id":"0f85baaf-a5cc-44f5-838f-976b9d1ceda3","resolution":{"observed_at":"2026-08-08T19:18:02.996547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.970625Z","title":"Proximité et dualité dans un espace hilbertien,","venue":null,"work_id":"d3a9ddd2-a8b5-46a3-b864-e0530d375490","year":1965},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.651578Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:e618f3bca9e5eaeb4e3e185e75cb4db64200fb7d152a3ca45d1e6fcb83db17ef","observation_id":"ef8e9753-9357-4f53-8a3c-aa7ed948d16e","resolution":{"observed_at":"2026-08-08T19:18:02.975553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.656394Z","title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.656394Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:cf5eba6f4a8217e737c5600f01333724d2914a85ef8feb9edb8d6e33d0510a98","observation_id":"1b894a32-bb33-4776-b062-f0565b702a36","resolution":{"observed_at":"2026-08-08T19:18:02.656394Z","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":"2026-08-08T19:18:02.944888Z","title":"Few-shot semantic segmentation with proto- type learning","venue":null,"work_id":"8bc45f63-f999-467f-a11a-1caedc1ef5b3","year":2018},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.661238Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:ce01c863db830b9d934683327bc1242618807e5d6bf31181b80ab6da785e3f23","observation_id":"00a1b08d-d01c-479c-9b9c-f34b26bdf1e0","resolution":{"observed_at":"2026-08-08T19:18:02.950018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.927942Z","title":"Few-shot medical image segmentation via a region-enhanced prototypical transformer,","venue":null,"work_id":"16eb784a-abaa-48c4-bb0d-de4ecf9d3909","year":2023},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.665680Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:0c2a647d8b66f6814339e76f12fa6cb683f4dd5c4b99728d43cc39421004b920","observation_id":"b6ea00a8-165a-4fa4-af9b-c15d41b85a8b","resolution":{"observed_at":"2026-08-08T19:18:02.932756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.912623Z","title":"Anomaly detection-inspired few-shot medical image segmentation through self- supervision with supervoxels,","venue":null,"work_id":"2521e158-a8dc-4b3d-8bd5-f66a727e0139","year":2022},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.670172Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:b5d495e26e73f4367a0d7fc82501759d2220e41fe4b41434b1411196e0931d53","observation_id":"960aa7a2-d0b8-42ae-b3e9-7111cc0fd8e8","resolution":{"observed_at":"2026-08-08T19:18:02.917558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.675244Z","title":"Intermediate prototype mining transformer for few-shot semantic segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.675244Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:96b577c3e35459471576b09f41bc443e5c6fbf4c91c7e221a60799c3cbb50dd6","observation_id":"a708515e-bca2-46ec-8627-8b4a47f845c4","resolution":{"observed_at":"2026-08-08T19:18:02.675244Z","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":"2026-08-08T19:18:02.884149Z","title":"Self- supervision with superpixels: Training few-shot medical image seg- mentation without annotation,","venue":null,"work_id":"e48b275e-3383-4a3d-98c4-e4c3ba54f413","year":2020},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.680738Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:d111f5fd4b98d4ccc9c2d225e414f349770af935c2f73a0b6b4f99eeb36749ca","observation_id":"720a2973-cbdb-4b32-a66f-7139866386a3","resolution":{"observed_at":"2026-08-08T19:18:02.890164Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.867927Z","title":"Rethinking few-shot medical segmentation: a vector quantization view,","venue":null,"work_id":"b9a39c97-74df-4304-8044-4693074655be","year":2023},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.685503Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:5c87ca907c986243b8b1b476e11aeaec6fca5e5d04c317bcaf0e18921de35105","observation_id":"60ea553b-0043-432d-9902-40b249956897","resolution":{"observed_at":"2026-08-08T19:18:02.872556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.851583Z","title":"Recurrent mask refinement for few-shot medical image segmentation,","venue":null,"work_id":"97d74fcb-7178-4216-b02e-26d53419fd82","year":2021},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.689755Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:b3cf0e71a32e561538479e080103ad7d3727dbc41b2de5e6f9eca356c70b28bf","observation_id":"41e2f7a1-6d12-4f7c-bc80-0f17fd9e3b61","resolution":{"observed_at":"2026-08-08T19:18:02.857149Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.835910Z","title":"Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,","venue":null,"work_id":"42e1b983-0f98-4258-a397-49545f01fe49","year":2015},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.693970Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:49dcbf2bd5eabfb82ea438d35153fb2aa9c7452fe98966047dce983a6c49303d","observation_id":"0247f02a-1794-49ab-85d8-a2c508aae2c2","resolution":{"observed_at":"2026-08-08T19:18:02.840496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.820518Z","title":"Chaos challenge- combined (ct-mr) healthy abdominal organ segmentation,","venue":null,"work_id":"2c0b3c18-14c7-42dd-873f-5022889763d6","year":2021},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.698768Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:fdbfe2c7b8e235787bd6e775a887dd83059fd842b622ec9b9a76b51607e0ad08","observation_id":"632f4c34-7021-4c4b-9407-f3d0110737bc","resolution":{"observed_at":"2026-08-08T19:18:02.825390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.804494Z","title":"Multivariate mixture model for myocardial segmentation combining multi-source images,","venue":null,"work_id":"c64ea1e8-e940-4d72-a0b5-769c4e6ba390","year":2018},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.702868Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:ebacba35a23bf92a6d64d64f7befff2a3a8884a753513cac60eec4cb12bdf7ea","observation_id":"ea8f6534-4826-4d0a-b2cc-b8e73a0f0702","resolution":{"observed_at":"2026-08-08T19:18:02.809592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-08T19:18:02.707224Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.707224Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:0a2d9dd2ae939639c9f24cb35b1655c966e0eb9053306b4b05fcdb911f98a945","observation_id":"de5ee809-a081-4313-b9de-4e56f7959a61","resolution":{"observed_at":"2026-08-08T19:18:02.707224Z","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":"2026-08-08T19:18:02.711648Z","title":"Microsoft coco: Common objects in context,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.711648Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:31c8d7129d22170bb7292028efbfdfea449ce762a2bc90da979afff61c163859","observation_id":"c34c0d6b-8962-4770-848e-9693eb6c88fc","resolution":{"observed_at":"2026-08-08T19:18:02.711648Z","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":"2026-08-08T19:18:02.765777Z","title":"Large-scale machine learning with stochastic gradient de- scent,","venue":null,"work_id":"019181ec-fe60-4051-a600-cd5eab10073c","year":2010},"citing_paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T19:18:02.715681Z"},"links":{"citing_paper":"/paper/2502.05473"},"observation_digest":"sha256:1c1ace06148957d2397625df20eff37d729d62c728319c48edea54dc52f91294","observation_id":"3df4969f-7595-4529-9797-720fd3db9936","resolution":{"observed_at":"2026-08-08T19:18:02.772621Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.05473","last_updated":"2025-02-08T07:15:44Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T23:17:58.470579Z","submitted_at":"2025-02-08T07:15:44Z","title":"LMS-Net: A Learned Mumford-Shah Network For Few-Shot Medical Image Segmentation"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":36},"total_outbound_references":43},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2502.05473."}