{"as_of":"2026-08-09T09:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d6165a3e9f850e1312ff18fdc80ede458d81707f10936c2643d8f9e316018b80","coverage":[{"denominator":65,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":65,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:28:49.239733Z","state":"measured"},{"denominator":65,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":65,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.15861/citation-record","integrity":"/paper/2505.15861/integrity","json":"/paper/2505.15861/citation-record.json","paper":"/paper/2505.15861"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:29:04.752318Z","title":"Clinically applicable deep learning for diagnosis and re- ferral in retinal disease.Nature medicine, 24(9):1342–1350, 2018","venue":null,"work_id":"5fd09f93-0bb6-47b4-88ee-1c055cfecbf0","year":2018},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:41.439162Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:2c74a36ea719f0f771b6be299598379707edc39994873d59adfb070f083df833","observation_id":"f8d29b5b-9a79-4321-a6c3-c18478182a41","resolution":{"observed_at":"2026-08-07T15:29:04.875272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:04.481854Z","title":"Video-based ai for beat-to-beat assessment of cardiac func- tion.Nature, 580(7802):252–256, 2020","venue":null,"work_id":"b4eb551b-b532-45d6-9146-1bbebd57283e","year":2020},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:41.541155Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:24482b80c844150bc71a3b9e514bc41e3394cb44333d67adfbd8491d3cec2694","observation_id":"1fbceb53-9e1f-4c84-b9c9-102177f44b69","resolution":{"observed_at":"2026-08-07T15:29:04.616853Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:04.237800Z","title":"Bidirectional copy-paste for semi-supervised medical image segmentation","venue":null,"work_id":"618e691a-0bda-4613-bb82-62bd3278f135","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:41.672804Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:feeecf119ef02e1c198e4b57e376c23a0ebc636ab4007e2c51d8b820dcd221c0","observation_id":"adef5a10-78b8-4191-af06-6215bf2e1d76","resolution":{"observed_at":"2026-08-07T15:29:04.341366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:03.974898Z","title":"Mcf: Mutual correction framework for semi-supervised medical image segmentation","venue":null,"work_id":"d51c30de-1e37-4969-95b5-4cd1aa24b216","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:41.813347Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:940ce58039b2ffe99c77415fda32cf265760883a69a050c4abcd906895204c46","observation_id":"fbdc1ff3-5d79-4924-bfed-31f5654b76eb","resolution":{"observed_at":"2026-08-07T15:29:04.115648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:03.740920Z","title":"Pseudo-label guided contrastive learning for semi-supervised medical image segmentation","venue":null,"work_id":"2bfde9df-d304-41e5-92d4-18d7f6a7e0c3","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:41.939796Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:b8e0d9cc26e855b6e1e25713654929cb9a9b9ea5cabe52537d837255d3b3474a","observation_id":"e29ec6f5-31dd-4dec-a3e8-6d83bccd561b","resolution":{"observed_at":"2026-08-07T15:29:03.849336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:03.444248Z","title":"Orthog- onal annotation benefits barely-supervised medical image segmentation","venue":null,"work_id":"a5187a07-8c06-4f9e-b9d8-6780444c8f61","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.028769Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:bc26839ed6293113fcb2837862edffe6fd20de62d3548d7524802facad27c923","observation_id":"13fef9b3-7410-405c-a1c9-070a9a9ba84f","resolution":{"observed_at":"2026-08-07T15:29:03.563356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:03.245156Z","title":"Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium seg- mentation","venue":null,"work_id":"b6e60813-b6d5-4b8e-a1ec-83ef08a2f609","year":2019},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.143624Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:05bf4fa6d2e06c6a8d78790bdce15ecf06ca49e5dc004d9bcab99448dba3cef1","observation_id":"57f6a7e4-c523-43ba-86e2-9b9cfeff8dd1","resolution":{"observed_at":"2026-08-07T15:29:03.354301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:02.960075Z","title":"Shape-aware semi-supervised 3d semantic segmentation for medical images","venue":null,"work_id":"fa2888ec-fbdc-4a81-a7e8-f3682506d338","year":2020},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.251454Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:30dda6bc31bc35d081aec3e6d6dcbe4aa9ec819f43843b96b276ff3ea27cf420","observation_id":"4e8f0c92-5e2e-4b0f-93b5-4275ec1c299e","resolution":{"observed_at":"2026-08-07T15:29:03.068674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:02.696469Z","title":"Semi-supervised medi- cal image segmentation through dual-task consistency","venue":null,"work_id":"58637449-e57e-410f-b9ca-8fe175a92277","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.432062Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:c0887730f6c81782ba2b7d80386fac460295c56ae5f9aac87560392ff2dd4eb2","observation_id":"0c720d39-c8c2-4483-9345-91c0c7348b75","resolution":{"observed_at":"2026-08-07T15:29:02.824988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:02.440462Z","title":"Magicnet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery","venue":null,"work_id":"f92017e5-a2d3-483b-86db-d74d7c2c00a4","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.564299Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:79920a8adbf46c6978da192268c5a30c9f7a3c286c5ec8c8abfb6227bcaa2d1f","observation_id":"e8d1e197-d281-4f35-89e4-0dada2ec7318","resolution":{"observed_at":"2026-08-07T15:29:02.588413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:02.254722Z","title":"Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.Med","venue":null,"work_id":"335f388a-1c84-45b6-9487-5a29ecdc95c7","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.658022Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:c9df1470d52bd294bd8f55dfd3c9580dd13a5ee9346d61c232076b45c5934c83","observation_id":"1da0cd2e-fcbc-48f4-a4b0-deaa2b501e09","resolution":{"observed_at":"2026-08-07T15:29:02.303567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:01.973278Z","title":"Reference-guided pseudo-label generation for medical semantic segmentation","venue":null,"work_id":"ec9f0655-3854-459c-8eef-76eeb1b96026","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.765944Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:bc8209be9f3e4be1deb8d1d30ebcdf733c91aa335827bdd0f7793b43633f9c28","observation_id":"f05c84df-52a1-4285-b154-edc476f0271d","resolution":{"observed_at":"2026-08-07T15:29:02.108444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:01.675868Z","title":"Intra-and inter-pair consistency for semi-supervised gland segmenta- tion.IEEE Trans","venue":null,"work_id":"89655b45-9ea9-4c2d-84d1-5e7a28f3c903","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:42.986593Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:5ffc9aa57d471e1270cf3e821d366c4399769accf909977e89005d65f871a8c4","observation_id":"372f9016-d77e-4680-8a24-66ff8a0dcff4","resolution":{"observed_at":"2026-08-07T15:29:01.817407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:01.421824Z","title":"Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation","venue":null,"work_id":"896ee2b4-36ea-4d61-8087-b86e12589f71","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:43.136098Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:3346a14a8ff338d12ae7ac2a4c6d0eb72c0ceeac10d32b070989fb9138f53c3b","observation_id":"98f8e141-d4a0-4f07-92bc-8d81dbe3021b","resolution":{"observed_at":"2026-08-07T15:29:01.545027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:01.183750Z","title":null,"venue":null,"work_id":"628751a8-6b5c-4f18-bfec-8ed26c3304ed","year":2017},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:43.273741Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:844a6821e363a12fe953a92da67d0047b40eb63a641c7e9938cd1f49fe58900a","observation_id":"65b1686e-2694-478d-9ed0-431e8890d30d","resolution":{"observed_at":"2026-08-07T15:29:01.305886Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:00.985369Z","title":"Adaptive hierarchical dual consistency for semi-supervised left atrium segmentation on cross-domain data.IEEE Trans","venue":null,"work_id":"4333ea46-8f68-4f5e-8589-3ee3d6cfc81c","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:43.393172Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:c606d45d66806936a048c313cc225c16df150a6579b4cd1386bed4e85e13a285","observation_id":"e63d8487-eef4-4f85-9a45-159057c770c4","resolution":{"observed_at":"2026-08-07T15:29:01.092312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:00.746372Z","title":"Transformation-consistent self-ensembling model for semisupervised medical image segmentation.IEEE Trans","venue":null,"work_id":"77d2f37d-0d0c-448a-9c8a-0b86fbfb879c","year":2020},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:43.605628Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:055a8fd3d27e8900ba69cf33c4889f478bfd38776dfc517e31aefb9d797cd094","observation_id":"16d0954d-3b1a-41be-a21b-82d5161f46b0","resolution":{"observed_at":"2026-08-07T15:29:00.840417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:00.466948Z","title":"Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation.IEEE Trans","venue":null,"work_id":"386da329-ca31-4f7e-94aa-2b6ef4a4c239","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:43.736097Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:4bcf9b6531dcb95829410be71be168101c79b2bcabc40db4526b0d1431892771","observation_id":"10beb411-dda2-4f11-b2df-7772d8069d9f","resolution":{"observed_at":"2026-08-07T15:29:00.599268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:00.234530Z","title":"Mum: Mix image tiles and unmix feature tiles for semi-supervised object detection","venue":null,"work_id":"e85ee808-75cb-45dc-b696-0df563f519d0","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:43.917836Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:11ef31bf951f30b9636e8154a9e208a9084a9524604b3ec68041fa3b326152af","observation_id":"eb5bd785-884e-418d-818c-89666fa4bbd4","resolution":{"observed_at":"2026-08-07T15:29:00.341856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:29:00.031617Z","title":"Perturbed and strict mean teachers for semi-supervised se- mantic segmentation","venue":null,"work_id":"ae55827d-16d9-4d9f-ab06-b56f83509866","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.053911Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:96ddb48f2451f6a7a540bf33becbfb468dd8d786af1ca5c9544e7e8d504c1751","observation_id":"c7a7f057-e125-4d3e-8b49-2fb545cf5da5","resolution":{"observed_at":"2026-08-07T15:29:00.129244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:59.731151Z","title":"Instance- specific and model-adaptive supervision for semi-supervised semantic segmenta- tion","venue":null,"work_id":"ce3b7159-27ee-4787-b5cc-8ace0220f5d3","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.116837Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:7f225752b54192affcedde39bb21e4c8b033b8804badb5d2237b1926e38414d3","observation_id":"e0130166-be0a-4906-a5a5-94c5fa562649","resolution":{"observed_at":"2026-08-07T15:28:59.821498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:59.474674Z","title":"Revisiting weak-to-strong consistency in semi-supervised semantic segmentation","venue":null,"work_id":"8123df6e-8f65-4d58-8fca-261f8403c170","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.265755Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:1357b64d1375a4417dc772b4d6c33c9c1b4aa07677ab03726952e756d9b28454","observation_id":"c1a41728-b614-4651-a08d-364eb3daaaac","resolution":{"observed_at":"2026-08-07T15:28:59.623731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:59.193458Z","title":"Augmentation matters: A simple-yet-effective approach to semi-supervised se- mantic segmentation","venue":null,"work_id":"1868beff-6974-451a-9b46-dd31ef9fa774","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.355747Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:bea9ab67c465f93426e2d6215615e97fcf440c5318548faefd0f9d328eef82bb","observation_id":"8b58615f-3ea3-4978-8bce-b50d8260d82f","resolution":{"observed_at":"2026-08-07T15:28:59.319517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:58.847822Z","title":"Dsp: Dual soft-paste for unsupervised domain adaptive semantic segmentation","venue":null,"work_id":"2b679a38-1701-46c0-923c-90acf7d52ab7","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.479415Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:afc504b0c42c8a1f28ce3a117b3bc262f099fe4e751d2b94c69034ca28cbfe86","observation_id":"accbe04d-c9d3-4820-ac81-1a6789723685","resolution":{"observed_at":"2026-08-07T15:28:59.055638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:58.522549Z","title":"Class- mix: Segmentation-based data augmentation for semi-supervised learning","venue":null,"work_id":"14e685f2-cb47-4250-a7fe-4eadc0d8f70b","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.635687Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:2ce6b55fb0d6610ed80ee4073c5b162e366afc90d34cb64f0f230bd24b42fadb","observation_id":"61b4a0a4-c8c4-4458-946a-5af980feb36a","resolution":{"observed_at":"2026-08-07T15:28:58.697953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:58.232751Z","title":"Cutmix: Regularization strategy to train strong classifiers with localizable features","venue":null,"work_id":"4dc15355-0888-43ea-a83a-8bea5a535fb0","year":2019},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.760540Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:d2be919eb8a5795fa8e2b87195b0ec1d24ad076af98c70871f46557fe96b8ac6","observation_id":"c67e4fea-659c-40e2-a3c9-d5a97b2dcdc7","resolution":{"observed_at":"2026-08-07T15:28:58.357278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:57.876776Z","title":"Remind your neural network to prevent catastrophic forgetting","venue":null,"work_id":"d2e93868-54fb-4189-9d63-2627c363318a","year":2020},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:44.923008Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:2c073350be529010c3b3a5052874f17a917a8aa05d90824d150adca2255d6b93","observation_id":"f1257846-68a0-43ef-8b6b-36e419b399b6","resolution":{"observed_at":"2026-08-07T15:28:58.070064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:57.584958Z","title":"Tripled-uncertainty guided mean teacher model for semi-supervised 25 medical image segmentation","venue":null,"work_id":"222a606c-36bc-4b08-949c-f3b9482a35dd","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:45.030882Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:c8e95bea51927abb1ac99f92dcba20bfbecdfc7f90dc1688e7d9fd3029a2abee","observation_id":"1007a36a-dcd5-415c-803a-429c104290c1","resolution":{"observed_at":"2026-08-07T15:28:57.702245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:57.258709Z","title":"Semi-supervised neuron segmentation via reinforced consis- tency learning.IEEE Trans","venue":null,"work_id":"e3a135b4-7a2e-4748-895e-656c5dff1786","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:45.156458Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:350539c2c7a72dcc5ace62b71f25d68681f4c03090bfc9d0ab5b850e92016ab6","observation_id":"374ef793-9e76-4f16-8d88-cd3833737390","resolution":{"observed_at":"2026-08-07T15:28:57.413640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:57.011336Z","title":"Dacs: Domain adaptation via cross-domain mixed sampling","venue":null,"work_id":"a9b111a4-36f1-4c9f-8153-aa86551c7e92","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:45.328081Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:d2372ab458f605028d170e3d49fd0bb3e10919088154e05e5ce05fbaac234b44","observation_id":"9d141952-fb28-43c8-8ae0-1564bd16c90f","resolution":{"observed_at":"2026-08-07T15:28:57.121084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T15:28:45.423144Z","title":"mixup: Beyond empirical risk minimization.arXiv:1710.09412, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:45.423144Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:f216b1773b448780be39b5ee3d61180994c0444a76c167f32d8bd829bdb4c037","observation_id":"00e1f04e-292a-427e-ab99-fc1177fd5bda","resolution":{"observed_at":"2026-08-07T15:28:45.423144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.08430","last_updated":"2021-08-06T03:22:14Z","snapshot_observed_at":"2026-07-06T11:30:06.143581Z","submitted_at":"2021-07-18T12:55:11Z","title":"YOLOX: Exceeding YOLO Series in 2021","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.08430","snapshot_observed_at":"2026-08-07T15:28:45.586122Z","title":"Yolox: Exceeding yolo series in 2021.arXiv:2107.08430, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:45.586122Z"},"links":{"cited_paper":"/paper/2107.08430","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:9a7061b313349599f01b4c8eb82c0f0740b1e2d05f8184ebe3035e0c7642b964","observation_id":"70462a19-6ee4-4198-b7f4-b6827c333739","resolution":{"observed_at":"2026-08-07T15:28:45.586122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-07T15:28:45.730886Z","title":"Improved regularization of convolu- tional neural networks with cutout.arXiv:1708.04552, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:45.730886Z"},"links":{"cited_paper":"/paper/1708.04552","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:93b014e7dc49f1ef93536783db0eeeff880c857db8903f2a8c36cafc0f30eeb4","observation_id":"b1a333a0-652f-452c-853e-34d6055d6b14","resolution":{"observed_at":"2026-08-07T15:28:45.730886Z","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":"2026-08-07T15:28:56.757907Z","title":"Interpolation consistency training for semi- supervised learning.Neural Networks, 145:90–106, 2022","venue":null,"work_id":"d1b43f6a-f25e-4f76-8f8e-bf65a7d22d20","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:45.881753Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:bb0c8354d7c40f2aebe7c5d9fa6970f6ec0bf991a1026e894bd007280ba506d7","observation_id":"2433fd77-4614-4bc6-8794-67057a202046","resolution":{"observed_at":"2026-08-07T15:28:56.869852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:56.449401Z","title":"Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":"5dd5c98c-f254-4fd3-95ed-52a2f89b64b8","year":2019},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.060325Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:868e7a614e307d02089f761ac8f34d53a9078c675701c935d8ec0d0335c3379c","observation_id":"d949184b-4088-4ed6-8944-b1f68f9b1459","resolution":{"observed_at":"2026-08-07T15:28:56.562865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09785","last_updated":"2020-02-13T23:14:46Z","snapshot_observed_at":"2026-08-05T08:24:49.625500Z","submitted_at":"2019-11-21T23:44:25Z","title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09785","snapshot_observed_at":"2026-08-07T15:28:46.192382Z","title":"Remixmatch: Semi-supervised learning with dis- tribution alignment and augmentation anchoring.arXiv:1911.09785, 2019","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.192382Z"},"links":{"cited_paper":"/paper/1911.09785","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:edb2caf7b0bf5f9d9ba9e8c044e7b4c6db1736dddb65f9d65307ebd5b2993b74","observation_id":"9745da6a-3eda-4da7-8405-52c86e2a37fa","resolution":{"observed_at":"2026-08-07T15:28:46.192382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.01916","last_updated":"2020-08-11T16:23:40Z","snapshot_observed_at":"2026-07-06T07:58:06.375198Z","submitted_at":"2019-06-05T10:09:27Z","title":"Semi-supervised semantic segmentation needs strong, varied perturbations","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.01916","snapshot_observed_at":"2026-08-07T15:28:46.333913Z","title":"Semi-supervised semantic segmentation needs strong, varied perturba- tions.arXiv:1906.01916, 2019","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.333913Z"},"links":{"cited_paper":"/paper/1906.01916","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:51aca8cd3d7c5cc8b97a52837eddf63618cf8ce6ae5dec0dbe2521e195963618","observation_id":"261624f5-74fe-4978-85b6-20caa6c12b99","resolution":{"observed_at":"2026-08-07T15:28:46.333913Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04647","last_updated":"2021-11-22T04:22:49Z","snapshot_observed_at":"2026-08-04T03:11:00.283958Z","submitted_at":"2020-01-14T07:08:45Z","title":"Structured Consistency Loss for semi-supervised semantic segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04647","snapshot_observed_at":"2026-08-07T15:28:46.467744Z","title":"Structured consistency loss for semi-supervised semantic segmentation.arXiv:2001.04647, 2020","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.467744Z"},"links":{"cited_paper":"/paper/2001.04647","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:c3bf8c3350a9a17fef5b615b41b1f991df22763486871eee7c5b1cd76573b98e","observation_id":"697ea9bb-1f00-4592-ad07-f67c8d872d8e","resolution":{"observed_at":"2026-08-07T15:28:46.467744Z","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":"2026-08-07T15:28:56.184205Z","title":"Optimization of repetition spacing in the practice of learning.Acta neurobiologiae experimentalis, 54:59–59, 1994","venue":null,"work_id":"3dc7d3ff-32b7-49a9-993e-bc6dd932e9b7","year":1994},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.608287Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:cf2c9b7e729058bab6e5e814209ef2a31f20868f2831ee6caafc28d0ad406ff1","observation_id":"fd7ed569-8be0-42c8-8912-4e3c64d271e2","resolution":{"observed_at":"2026-08-07T15:28:56.315371Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:55.905517Z","title":"Memory: A contribution to experimental psychology","venue":null,"work_id":"64fde96f-703d-4f3e-b56c-84d0ccb4dc40","year":null},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.781947Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:2b4fab2aa95b81f4f3d199dd680b374a8d0d76928292bdfb172a17ac41fdfef9","observation_id":"41da4060-2e7f-4cd2-a552-99e3d5621de8","resolution":{"observed_at":"2026-08-07T15:28:56.026682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:55.650014Z","title":"A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.Med","venue":null,"work_id":"93c4b5e6-1243-4935-8bbd-aea989cf83d0","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.864101Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:cedd8d2133e6bfd68ea8534e62f2b2208a9f78f53a645dcac6838a64d96b7c9f","observation_id":"0cb860c5-0b72-40d8-afc5-58c7a1252682","resolution":{"observed_at":"2026-08-07T15:28:55.748104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:55.343589Z","title":"Explor- ing smoothness and class-separation for semi-supervised medical image segmen- tation","venue":null,"work_id":"0fa45933-5611-408e-b045-78b667f64909","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:46.955144Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:369ec4745b12c3e1f187904ee7701b4d047fdba7c7cbc66aa7dbe98b94c2414a","observation_id":"c87e6129-056e-4db4-9d09-ccaebd758be1","resolution":{"observed_at":"2026-08-07T15:28:55.473779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:55.039729Z","title":"Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation","venue":null,"work_id":"3973e1e4-6250-42b3-9648-eb76a465c4eb","year":2015},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.069079Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:3f57d299e86fdec696540f7a6254d6eaf100e8a890308197bd47dd7ad44575d1","observation_id":"7e74ab6b-4a5e-4b6a-8057-b393f34149d1","resolution":{"observed_at":"2026-08-07T15:28:55.156010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:54.733821Z","title":"Deep learning techniques for automatic mri cardiac multi- structures segmentation and diagnosis: is the problem solved?IEEE Trans","venue":null,"work_id":"103bcb13-07e4-4399-836d-ed93a6455b60","year":2018},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.173936Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:e0dfc625706325f7ae827ebba80e5af2ca8f00114554c459c239cca174d41223","observation_id":"d6d85ea9-fbc5-4346-94ba-5d9220f4b155","resolution":{"observed_at":"2026-08-07T15:28:54.873401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:54.417482Z","title":"Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in 27 histopathologic images","venue":null,"work_id":"b0bcd1b5-6030-4f30-9b2e-f6b71b3d2e2c","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.286786Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:7112ced2d3650deb2b04a3d2c6477cf33a98bc5036fd5ecf829ae0d6aa7fe7bb","observation_id":"73c99944-e043-4616-976f-c17fa8efb961","resolution":{"observed_at":"2026-08-07T15:28:54.546974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:54.097922Z","title":"SSL4MIS.https://github.com/HiLab-git/SSL4MIS, 2020","venue":null,"work_id":"84dcaae3-a1bf-4455-b8cd-e8dd6ef1c24c","year":2020},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.377781Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:78f83b23fc13496426bf9b4590e2b98ae2feb3cb6ed377f01739eafd0e9b6a8d","observation_id":"d01eb282-d07c-457c-b280-54944d7f3036","resolution":{"observed_at":"2026-08-07T15:28:54.256894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:47.503218Z","title":"The medical segmentation decathlon.Nature communications, 13(1):4128, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.503218Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:5d6bde1682ffc69160f2fa28f1cdf8d949a3c124fc317ac4640fb9e268874d28","observation_id":"e65bdaf2-a8f0-4cf4-ab9f-ff23459ee7ff","resolution":{"observed_at":"2026-08-07T15:28:47.503218Z","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":"2026-08-07T15:28:53.766854Z","title":"Cat: Constrained adversarial training for anatomically- plausible semi-supervised segmentation.IEEE Trans","venue":null,"work_id":"ba08a9b9-87a0-4ba3-ae35-d7f604b07214","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.649133Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:60f906d93f06a370f11d7ea588ded8ccfdce037f2afedae8f802833a1a2e9559","observation_id":"cd635d8b-bb1f-41da-bc80-a28ee5bdaa1a","resolution":{"observed_at":"2026-08-07T15:28:53.886954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1610.02242","last_updated":"2017-03-15T14:22:41Z","snapshot_observed_at":"2026-08-01T18:35:12.430501Z","submitted_at":"2016-10-07T12:15:42Z","title":"Temporal Ensembling for Semi-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.02242","snapshot_observed_at":"2026-08-07T15:28:47.793242Z","title":"Temporal ensembling for semi-supervised learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.793242Z"},"links":{"cited_paper":"/paper/1610.02242","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:594e430f7810f788576520ec3df15c09d1efa3e158d890938626ffbb6fe76988","observation_id":"42d9fb00-a6b0-4bca-9f41-1c3915d56959","resolution":{"observed_at":"2026-08-07T15:28:47.793242Z","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":"2026-08-07T15:28:53.534570Z","title":"Semi-supervised semantic segmentation with cross pseudo supervision","venue":null,"work_id":"b124c3b3-4531-4b55-a4b5-54401071accb","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:47.910736Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:7ffc0f904c5e0ecefbed2b8ae34a8d9e48e637abf7631f01cc7fcfac711e1639","observation_id":"dfe9570a-ef67-4ab5-b333-fbaeb371e44d","resolution":{"observed_at":"2026-08-07T15:28:53.650315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:53.230891Z","title":"Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation","venue":null,"work_id":"ce200e26-2541-41b2-82e8-d8ec1a087901","year":2021},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.019519Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:b5977381c497a99350ebfb80488add9620c4a95c5dd7885286e6ee5479ab8355","observation_id":"ad777d01-818b-4d17-b05e-378de5bdf340","resolution":{"observed_at":"2026-08-07T15:28:53.382817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:52.923346Z","title":"Semi-supervised semantic seg- mentation with cross-consistency training","venue":null,"work_id":"094a0428-04f0-46af-9788-d96b59eb86bf","year":2020},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.122076Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:15f5ecea8d3ab019aaa469a4e957b40811429ea6151d39afaa380cc1c94e8b8e","observation_id":"ac414e9a-504d-4e57-810d-7ee630d2ec59","resolution":{"observed_at":"2026-08-07T15:28:53.056752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:52.596375Z","title":"Fully convolutional neural networks for volumetric medical image segmentation","venue":null,"work_id":"6476aa31-b276-4a98-a822-d49cc061b8fe","year":2016},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.216216Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:fdb250b02fdc5a45eed419661ab38106e2c973361d88c49b099387c49c8dc3a2","observation_id":"ee76f4fe-bc51-4514-be80-388bf846c2c0","resolution":{"observed_at":"2026-08-07T15:28:52.741720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:52.191503Z","title":"U-net: Convolutional net- works for biomedical image segmentation","venue":null,"work_id":"48274490-05f4-4d7f-8d50-5f9b5e80ee09","year":2015},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.285866Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:ebf9e823b23f3c1df3dc992e653c1cb9f1dd905b716264ab7122d966a3694b7b","observation_id":"26d9b38f-beaf-435f-9f61-b4f90ac7d88b","resolution":{"observed_at":"2026-08-07T15:28:52.398324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:51.933341Z","title":"Correlation- aware mutual learning for semi-supervised medical image segmentation","venue":null,"work_id":"8909599e-61f6-4c78-9ce2-e121187bf2d7","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.355584Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:67698f1e466c9631a542b582cab426945a9174fbfba6630be9f0429377a431af","observation_id":"fc913a35-9db7-4b23-a96c-f4ebfc3378f3","resolution":{"observed_at":"2026-08-07T15:28:52.046598Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:51.628198Z","title":"Upcol: Uncertainty-informed prototype consistency learning for semi-supervised medical image segmentation","venue":null,"work_id":"cef0b011-93b6-4976-9c3e-429640ca5fa0","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.433707Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:581c0c5c067a51fa12309dce131e41a4d2e627998cd5e43da258a2d60adf9b4a","observation_id":"964e1ab3-37e0-4b91-9b3b-7ecda881e83a","resolution":{"observed_at":"2026-08-07T15:28:51.749570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:51.371177Z","title":"Combinatorial cnn-transformer learning with manifold constraints for semi-supervised medical image segmentation","venue":null,"work_id":"a60cef98-7ae8-4752-a92f-9cafe2799514","year":2024},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.495078Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:1076cb9bd88c743f7161acd9fd135ea9bde6b6536f280b4fd0a0b36810128611","observation_id":"12db9dbf-5ac0-4571-817f-b146b903335b","resolution":{"observed_at":"2026-08-07T15:28:51.512519Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:51.064692Z","title":"Consistency-guided differential decoding for enhancing semi- supervised medical image segmentation.IEEE Transactions on Medical Imaging, pages 1–1, 2024","venue":null,"work_id":"b1dd8078-ce34-4ec4-b67d-7fd36c3fdcbd","year":2024},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.634304Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:2b3fea52a0479e67ba9e44b8e970288f357c19c3369254fc47719a2952125665","observation_id":"164d8016-cbc6-4c31-81a0-2b290cf1c91f","resolution":{"observed_at":"2026-08-07T15:28:51.231401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:50.831173Z","title":"Pmt: Progressive mean teacher via exploring temporal consistency for semi-supervised medical image segmentation","venue":null,"work_id":"e8bfb2a2-445b-43e8-8f1b-1d385db6643e","year":2025},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.754323Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:e393e362c9df3547b4e10616fb682727eea91d59c287810fb37747b80eceba15","observation_id":"8fee530e-e630-4b9f-8b32-c31de4ceff41","resolution":{"observed_at":"2026-08-07T15:28:50.917003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:50.668257Z","title":"Decoupled consis- tency for semi-supervised medical image segmentation","venue":null,"work_id":"a50c6236-c054-413b-961f-920d0e86fdd8","year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.845882Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:315ccc7dc77f5cff1782a22b1dadb15bfa30b586a6473bc4fe3a9a99cd86d19c","observation_id":"b0041412-ee65-4bcc-8aad-0f5259dc9a52","resolution":{"observed_at":"2026-08-07T15:28:50.732397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:50.379567Z","title":"Semi-supervised medical image segmentation using cross-model pseudo-supervision with shape awareness and local context constraints","venue":null,"work_id":"10b9e908-5460-4c9c-a75e-6beda4bc8a93","year":2022},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:48.941045Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:0a27ec2959a04ad15329e5a7ea3ea7fc46e610b17cf121b35feeb1a848faab18","observation_id":"15fd4d1c-c3fa-4d31-8d29-537178125fdd","resolution":{"observed_at":"2026-08-07T15:28:50.554852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.16214","last_updated":"2023-05-25T16:22:04Z","snapshot_observed_at":"2026-08-06T08:23:02.158472Z","submitted_at":"2023-05-25T16:22:04Z","title":"Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.16214","snapshot_observed_at":"2026-08-07T15:28:49.008978Z","title":"Self-aware and cross-sample prototypical learning for semi- supervised medical image segmentation.arXiv:2305.16214, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:49.008978Z"},"links":{"cited_paper":"/paper/2305.16214","citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:3c4528c7ca465adb6c2636aa3777833127fbe84ef349a1dcbb24b8d4dd766058","observation_id":"fab37e5d-2689-45c7-9047-132976b53575","resolution":{"observed_at":"2026-08-07T15:28:49.008978Z","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":"2026-08-07T15:28:50.100801Z","title":"Adaptive bidirec- tional displacement for semi-supervised medical image segmentation","venue":null,"work_id":"b3ae27ad-1dc5-4225-abc2-7b61b51433da","year":2024},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:49.087026Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:ba6b83ec1037e3f0d5d605ba40779d3a89df8c5af3873380e43ebc0d9a233d55","observation_id":"e85a1243-4b72-4a34-be8e-198d43002f41","resolution":{"observed_at":"2026-08-07T15:28:50.243416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:49.834465Z","title":"Gradient-aware for class-imbalanced semi- supervised medical image segmentation.pancreas, 98:1–86","venue":null,"work_id":"dab37a26-4f02-48eb-9117-311d1a3cfe49","year":null},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:49.152057Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:f9780505e58ac3a3e01cb03123687d9d4b578cb8a750acfc5eefa8bbe16f074b","observation_id":"724ca80c-81e6-42b5-a4ce-59ff228101c3","resolution":{"observed_at":"2026-08-07T15:28:49.956065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T15:28:49.537108Z","title":"Alternate diverse teaching for semi-supervised medical image segmen- tation","venue":null,"work_id":"a0b562bb-5a22-4e2e-ad12-117f7d85c06f","year":2025},"citing_paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T15:28:49.239733Z"},"links":{"citing_paper":"/paper/2505.15861"},"observation_digest":"sha256:cb7c3f2a4b0dd09b27a9eb5b0370557adbc49b09568473854437aedf021773d5","observation_id":"18f6d9d5-c7e3-4978-b0ef-d3c6ac798f50","resolution":{"observed_at":"2026-08-07T15:28:49.677525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.15861","last_updated":"2025-05-21T05:35:28Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-07T15:21:02.442232Z","submitted_at":"2025-05-21T05:35:28Z","title":"P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation"},"reference_resolution":{"displayed":65,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":55},"total_outbound_references":65},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2505.15861."}