{"as_of":"2026-08-22T16:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:222416511a8183855756f979ef6220bf57f2d5739d404859c40ce9d8a55228b8","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-03T15:57:58.162934Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2607.02051/citation-record","integrity":"/paper/2607.02051/integrity","json":"/paper/2607.02051/citation-record.json","paper":"/paper/2607.02051"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T05:50:44.747425Z","title":"Semi- supervised information fusion for medical image analysis: Recent progress and future perspectives.Information Fusion, 106:102263, 2024","venue":null,"work_id":"db02d5e0-d836-40c9-a478-107058398907","year":2024},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:8469ed156c66e2c1a961d501048277282bfd8561ceaac8cbe869ef3b784a964b","observation_id":"6b5cbcfc-274a-48cb-9f48-f863d8ea6867","resolution":{"observed_at":"2026-07-05T05:50:44.748778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.749515Z","title":"Deep semi-supervised learning for medical image segmentation: A review.Expert Systems with Applications, page 123052, 2024","venue":null,"work_id":"1bc8164f-5096-4b7a-a4aa-fdc90409dba3","year":2024},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:2ba7dc005bcda41f7cbeed641327b9a736a532827cf4549b3310dc5bc38150b2","observation_id":"af5adb24-bf2a-4eeb-86f2-39f1067470db","resolution":{"observed_at":"2026-07-05T05:50:44.750848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.727540Z","title":"Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation","venue":null,"work_id":"e6f11ef2-571f-421b-ab10-23da4f04a0e8","year":2023},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:9dfd7343e20c72812d0e755f9e85470b696ad053adf14855ecee56b2ac7a13fb","observation_id":"b15443c0-aa20-49df-b712-2b3a5ec359fb","resolution":{"observed_at":"2026-07-05T05:50:44.728884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.735341Z","title":"Uncertainty-aware self-ensembling model for semi- supervised3dleftatriumsegmentation","venue":null,"work_id":"fd9da89d-4860-4d78-9bb6-d51c563dddb0","year":2019},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:448cb9e886bb3f154a2880dff239fd7e34504f9981d9e474892b351b8920f834","observation_id":"6847dde3-7a38-455f-93be-71193d04e957","resolution":{"observed_at":"2026-07-05T05:50:44.736665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.745517Z","title":"Semi- supervised medical image segmentation through dual-task consis- tency","venue":null,"work_id":"138d1ce5-ea6a-4edb-9f92-6bd1732dbec7","year":2021},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:dd30b4fbe593423044054a69b2e1c35b712405236d113ae218d1c4e25d272134","observation_id":"8cc36663-9047-4812-9b00-06eb97257ba8","resolution":{"observed_at":"2026-07-05T05:50:44.746808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.771571Z","title":"Triple-task mutual consistency for semi-supervised 3d medical image segmentation.Computers in Biology and Medicine, 175:108506, 2024","venue":null,"work_id":"79329683-163d-4c62-a21d-11e16393ecda","year":2024},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:e157939f57d5bbc45f73a4b3f52efbb706570034fd5d7a3edbe2b099a9b70a3d","observation_id":"a2c8220f-33d0-4156-b1d0-bb29eceb94b9","resolution":{"observed_at":"2026-07-05T05:50:44.772937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.703193Z","title":"Upcol: uncertainty-informed prototype con- sistency learning for semi-supervised medical image segmentation","venue":null,"work_id":"eb970d0e-68a7-4476-8ff5-0f09ded9172b","year":2023},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:8810b8b86a02b4981cf465783432828f84e712235adf0d0063f66e67b08f4731","observation_id":"21cb2e67-21bd-4eaa-b558-2f673fe1f8fc","resolution":{"observed_at":"2026-07-05T05:50:44.704453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.709921Z","title":"All-around reallabelsupervision:Cyclicprototypeconsistencylearningforsemi- supervisedmedicalimagesegmentation.IEEEJournalofBiomedical and Health Informatics, 26(7):3174–3184, 2022","venue":null,"work_id":"f87ab573-8d0d-4e8f-832d-ee3afba4989c","year":2022},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:cb4995b160a1d47f28a76a4492ea91c867c835c87cd4ae586efd43bb1a5f27a9","observation_id":"2d566858-2e39-4f34-a9fb-609593e0883b","resolution":{"observed_at":"2026-07-05T05:50:44.711246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.721495Z","title":"Boundary-aware prototype in semi-supervised medical image seg- mentation.IEEE Transactions on Image Processing, 2024","venue":null,"work_id":"54b1dfdb-bfcf-4772-b2d8-f6fd03108da5","year":2024},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:00f25f06b930952a54cdf4d368b025c1405e8f82e3f1a9158d431b68c58f9606","observation_id":"4ada52b0-6336-4952-84d1-ba4e050089a6","resolution":{"observed_at":"2026-07-05T05:50:44.722739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.679878Z","title":"InICASSP 2025 - 2025 IEEE International Conference on Acoustics,SpeechandSignalProcessing(ICASSP),pages1–5,2025","venue":null,"work_id":"885d7c3e-3a96-4f48-9193-03d892397d78","year":2025},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:b18f76a4806c216eba6459d05eb4949841dd2fa5733b34bb196c9c112ff4b9ae","observation_id":"415596bf-b466-4ba0-9142-c1a7de5614ce","resolution":{"observed_at":"2026-07-05T05:50:44.681412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.682184Z","title":"Im- proving segmentation and detection of lesions in ct scans using intensity distribution supervision.Computerized Medical Imaging and Graphics, 108:102259, 2023","venue":null,"work_id":"feec4dbf-35a6-48ad-b5a9-7c8618696c35","year":2023},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:3a74edd64132171938422f3e0f01c87f7812054a92b16ec3017119977962ac0c","observation_id":"1ac520e3-a63f-4016-b65b-d5f3825b8edf","resolution":{"observed_at":"2026-07-05T05:50:44.683614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.684477Z","title":"Comprehensive evaluation of op- timization algorithms for medical image segmentation.Scientific Reports, 15(1):37190, 2025","venue":null,"work_id":"135272ab-64b5-450b-8415-67b71d08ff25","year":2025},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:474616a415be35ceeb0cf88212dd9ec19f097ca43c930c822e47cbff596b16dc","observation_id":"5d74f1bb-d06f-4f3b-b5ab-dc153b145b8f","resolution":{"observed_at":"2026-07-05T05:50:44.685796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.719663Z","title":"Gaussian mixture models.Encyclopedia of biometrics, 741(659-663):3, 2009","venue":null,"work_id":"a70caceb-fa79-4a9d-af80-be81b7826059","year":2009},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:22ca87621e26477a95f17568604d3c0ca08e8067b7f9be1193b07d7c9a606452","observation_id":"6c3935d1-35ef-4dd5-a7a4-2cf4d4ed4486","resolution":{"observed_at":"2026-07-05T05:50:44.720968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.675600Z","title":"Constrained gaussianmixturemodelframeworkforautomaticsegmentationofmr brain images.IEEE transactions on medical imaging, 25(9):1233– 1245, 2006","venue":null,"work_id":"05bf7312-eb91-49bf-80de-e9efbeb121bf","year":2006},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:2aeb8de7b866eae1cfe2f1c454584e99ee7d95129f35e93521f3c7674c6a1f9d","observation_id":"80af79a7-75cd-4192-bf3b-a7c1ed141e3b","resolution":{"observed_at":"2026-07-05T05:50:44.677277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.677971Z","title":"Cross-domain medicalimagetranslationbysharedlatentgaussianmixturemodel","venue":null,"work_id":"c9ecd709-9525-4fab-b7de-0fe4fd22ddb0","year":2020},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:e2972cbbe227095e0e0acf8b95d7da29223cfc3e28963aa8277ded87b9aa824d","observation_id":"65b6d4d2-725c-4a00-bec6-efcce3ac7437","resolution":{"observed_at":"2026-07-05T05:50:44.679308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.711840Z","title":"Rednet: Reliable evidential discounting network for multi- modality medical image segmentation.IEEE Transactions on Medi- cal Imaging, 2025","venue":null,"work_id":"90720ad5-9e88-437d-a50d-2f7b266b87be","year":2025},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:fef66b2dee19f7a7f66d99041b01457fc85b740d5153aa3d1e42e405234cbbce","observation_id":"bbafe142-0945-492b-8076-83669cac0e0d","resolution":{"observed_at":"2026-07-05T05:50:44.713101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.773554Z","title":"Target-aware u-net with fuzzy skip connec- tions for refined pancreas segmentation.Applied Soft Computing, 131:109818, 2022","venue":null,"work_id":"9669aca1-279b-43e2-8dcf-1eaa4a5290ee","year":2022},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:f2211304de671b024cfa8a286fdfcdfdfb3eb7256d99e0093c829c3d25fe6a7e","observation_id":"df2229c7-1320-4037-931c-33bf2684d716","resolution":{"observed_at":"2026-07-05T05:50:44.774858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.753360Z","title":"Medical image segmentation review: The success of u-net.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024","venue":null,"work_id":"d851b216-94d9-40f0-a4cc-fde1ad693e62","year":2024},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:88669a5f94bc0b7baa22ebb74793eaf56a8f567b5ba283e483d7dbfa0ef7136c","observation_id":"0d94def5-89ba-4873-8909-db52c9b0a93f","resolution":{"observed_at":"2026-07-05T05:50:44.770684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.743614Z","title":"D-edl: Differ- ential evidential deep learning for robust medical out-of-distribution detection.Medical Image Analysis, page 103888, 2025","venue":null,"work_id":"1d8e1b0b-9f8a-4dff-8525-74adef5d0f69","year":2025},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:660b1d819bcebc4d3fab773aebbed100b857c69b71a839da59d9633b6034c7fb","observation_id":"3332d2fa-fde8-4938-8aab-aefd4ada8614","resolution":{"observed_at":"2026-07-05T05:50:44.744977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.739295Z","title":"Category-specific unlabeled data risk minimization for ultrasound semi-supervised segmentation.Medical Image Analysis, page 103773, 2025","venue":null,"work_id":"e344907c-de06-477d-b2e5-5a304ac10efe","year":2025},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:e2cdba749aa6d0d3065d9ecd347cae5ab432d510d90863547a47f1d54ea00a47","observation_id":"9620a17e-5474-44c9-8a44-6f99831461f7","resolution":{"observed_at":"2026-07-05T05:50:44.740669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.20749","last_updated":"2025-02-28T05:54:41Z","snapshot_observed_at":"2026-08-16T12:54:24.149308Z","submitted_at":"2025-02-28T05:54:41Z","title":"SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models","version":1},"cited_work":{"arxiv_id":"2502.20749","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.20749","snapshot_observed_at":"2026-07-03T15:58:37.243371Z","title":"Semisam+: Rethinking semi-supervised medical image segmentation in the era of foundation models.arXiv preprint arXiv:2502.20749, 2025","venue":null,"work_id":"cc92b05e-d427-4ab1-99cf-1b213f1eaa14","year":2025},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"cited_paper":"/paper/2502.20749","citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:08fcc6a7d0118a17e933ea76966995b365af0b5d93d9fe1f9ea68bbeb00c30cc","observation_id":"ed5cc048-eeea-4eb5-a49f-76ad5d534c90","resolution":{"observed_at":"2026-07-03T15:58:37.244878Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.737324Z","title":"Semi-supervisedmedicalimagesegmentationviauncertainty rectified pyramid consistency.Medical Image Analysis, 80:102517, 2022","venue":null,"work_id":"97332deb-2c28-4463-8c5a-8628237905fb","year":2022},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:05393939fdf1dacab3fcd6a00790172e8d138c2f7bbbbd95e6ec9fb0f0b1899c","observation_id":"0337a305-679e-4165-9bbe-827bc3d9baed","resolution":{"observed_at":"2026-07-05T05:50:44.738660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.743491Z","title":"InIn- ternationalConferenceonMedicalImageComputingandComputer- Assisted Intervention, pages 481–491","venue":null,"work_id":"4f36e814-fd12-4e32-890f-3f745f58c08b","year":2022},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:447ab86dab2671dc3e2ae54809accd31ef75f2e95fad5e1cfa6d09834944593d","observation_id":"b764178c-80d7-4c7a-9297-5d2e3ba1a43f","resolution":{"observed_at":"2026-07-05T05:50:44.744771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.730948Z","title":"Temporal ensembling for semi- supervisedlearning","venue":null,"work_id":"10bc2859-c03f-4fb3-a0f9-7e751a309519","year":2017},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:320cdc8ab2776c85bf754900556b079359243e3f4eee1ff48e184bcf4ba248ca","observation_id":"6faa5bdc-5719-48ef-8361-75137300f093","resolution":{"observed_at":"2026-07-05T05:50:44.732487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.729446Z","title":null,"venue":null,"work_id":"2be7f259-1e9e-458f-82ff-b2f2da396f1d","year":2017},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:2427ec9632a7d37110f068dccb32152ccf99310234a65b21a723cef165e310c4","observation_id":"c42d9810-c29a-427d-8c43-8458f42286a7","resolution":{"observed_at":"2026-07-05T05:50:44.730736Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.723293Z","title":"Semi-supervised 3d medical image segmentation based on dual-task consistent joint learning and task-level regularization","venue":null,"work_id":"42499967-bab6-418d-8f2e-c449d10ab66c","year":2022},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:9fae5496eac83b0f5efa5d020d90a5eb534fecf920ab171124eaf32f4e403d25","observation_id":"0167b22c-a357-4eba-8b8d-bb8cb0f9318c","resolution":{"observed_at":"2026-07-05T05:50:44.724735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.731457Z","title":"Adaptive feature aggregation based multi-task learning for uncertainty-guided semi-supervised medical image segmentation.Expert Systems with Applications, 232:120836, 2023","venue":null,"work_id":"d83f4cfa-e4bf-47e2-a41f-daa58bf698a2","year":2023},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:ee00aca29d00936a10a17a962721d04ab0ea3d56b76292a9e60d01e08f954846","observation_id":"dfa7c62e-06fd-4e2d-aba7-78e8aadc26da","resolution":{"observed_at":"2026-07-05T05:50:44.732708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.733343Z","title":"Self- supervisedcorrectionlearningforsemi-supervisedbiomedicalimage segmentation","venue":null,"work_id":"c4c3e852-f3c4-4d1b-a974-b87bab21f9cd","year":2021},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:c5c71923af238ffcd666c5090d4e18017792d9f9fe8cbf6431b65af37549318b","observation_id":"efee3a4f-294a-41a1-bf92-ec33daf91b37","resolution":{"observed_at":"2026-07-05T05:50:44.734574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.751493Z","title":"Semi-supervised left atrium segmentation with mutual consistency training","venue":null,"work_id":"c3547df7-e151-4d25-9a18-a5f78cff9e2e","year":2021},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:4816808a67586e790c51ce4adfda68f0cbb00fad000b8c561a57db97a3b26939","observation_id":"d517a157-141e-4d1f-b4f8-1ab660fe6408","resolution":{"observed_at":"2026-07-05T05:50:44.752760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.715510Z","title":"Few-shot semantic segmentation with prototype learning","venue":null,"work_id":"d8093c30-6768-4574-898d-61f51357411b","year":2018},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:ebbf4a9c96c24a31a98b74990e4f982dc14d7ebc03b0b3ab47d3b6397f562989","observation_id":"58e9c052-a46d-4c42-82b9-42628c20b549","resolution":{"observed_at":"2026-07-05T05:50:44.716762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.713713Z","title":"Psanet: prototype-guided salient attention for few-shot segmentation.The Visual Computer, pages 1–15, 2024","venue":null,"work_id":"2b9ffa5a-a79a-418f-a9bd-5a9ba1f0f2ba","year":2024},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:2fba230905212e91aa704d586b9a55b6c3d455a575ddc409cf6c7ce1d7503101","observation_id":"f202ac9d-2994-4ea2-9a5a-9686978dcfa1","resolution":{"observed_at":"2026-07-05T05:50:44.714940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.717351Z","title":"Kp2l: Knowledge-driven pyramid prototype learning for semi- supervised medical image segmentation.Knowledge-Based Systems, 340:115662, 2026","venue":null,"work_id":"e8c7300e-b194-43e3-b611-a32e0022e1c4","year":2026},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:58189796bd1eb1e1fc910beae0bf591836b6f0dfd6170d2090ffe3fbe1adf194","observation_id":"db810a9c-2f68-4f56-be09-91c575983a6e","resolution":{"observed_at":"2026-07-05T05:50:44.719056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.718875Z","title":"Semi- supervised semantic segmentation with prototype-based consistency regularization.Advances in neural information processing systems, 35:26007–26020, 2022","venue":null,"work_id":"f384ba8f-6c80-43ef-b920-90d7dffef34e","year":2022},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:c4017fbedb02f5ee6315f5f91fdd18ff04040588713fcaa2ffc14040fec47189","observation_id":"184ce2fd-8793-4216-b9cf-0028aa96f447","resolution":{"observed_at":"2026-07-05T05:50:44.720283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.720848Z","title":"Adaptiveprototype learningand allocation for few-shot segmentation","venue":null,"work_id":"3cce2499-54aa-4f09-9bb3-db07c9299c5b","year":2021},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:30471f8489e6eb93649851f25c71d417fc437a12805e313f6b1d376a6f58f1f2","observation_id":"19b10085-b4df-44f2-b4ec-aa5411ec6fcd","resolution":{"observed_at":"2026-07-05T05:50:44.722244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.729088Z","title":null,"venue":null,"work_id":"0f00642b-80da-4fc5-b8c2-c28433ddac12","year":2023},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:745d9539a2a2b482473cfc22d221ef8d49820e578c4d0d185825b2d158b5ffb0","observation_id":"ff2d219c-30c7-4bfa-88f7-21e31f9fb308","resolution":{"observed_at":"2026-07-05T05:50:44.730322Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.699339Z","title":"Mvpcl: multi-view prototype consis- tencylearningforsemi-supervisedmedicalimagesegmentation.The Visual Computer, pages 1–14, 2024","venue":null,"work_id":"d0b818d8-cb53-471a-95a8-321304ae5e23","year":2024},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:7920b8588592e893367bc0b64a5a44089f73c607151c160a04afd592a33a3f09","observation_id":"7ab113d9-5d7c-4aeb-8cb9-65d32b884e40","resolution":{"observed_at":"2026-07-05T05:50:44.700608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.733134Z","title":"Focal loss for dense object detec- tion","venue":null,"work_id":"b2dad122-ab6a-4798-a324-4b0272c538ca","year":2017},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:d9fdd2d23d4217f6c753f579f94af2439d6f17c90e7ac3c8b633634cb2d288d4","observation_id":"32573bbf-929e-4eae-8827-54db39d48123","resolution":{"observed_at":"2026-07-05T05:50:44.734586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.741296Z","title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","venue":null,"work_id":"f7adf3bc-0336-4a38-b2a0-f7adba3b1a39","year":2016},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:04a452e5d7e3bf4b3b9cd816136c9e0d76b5603e6a69458ae5522fb344ca98dc","observation_id":"0ec349fe-2e4a-4cd2-82ce-00e14e7ddba3","resolution":{"observed_at":"2026-07-05T05:50:44.742996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.690656Z","title":"Unitbox:Anadvancedobjectdetectionnetwork","venue":null,"work_id":"5faabc7f-f68e-490c-87d0-5b975f5b6a26","year":2016},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:242cad4a6dd479dcfbd8938b674477bf483bcbf66abac30a9d2070ea249e22aa","observation_id":"bb273ca3-8592-4aa6-bd50-6926555b9406","resolution":{"observed_at":"2026-07-05T05:50:44.691885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.695081Z","title":"A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic res- onance imaging.Medical image analysis, 67:101832, 2021","venue":null,"work_id":"ce03ad82-b617-4d3b-9743-b4072ecd2b17","year":2021},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:bb59a47e473c329a0d63ee78237c8d93c13af05b32165660a8b63256adedebcb","observation_id":"2d5525d1-eb60-4180-b788-8aefbc1cf9ff","resolution":{"observed_at":"2026-07-05T05:50:44.696713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.688909Z","title":"Data from pancreas-ct","venue":null,"work_id":"2339b035-5763-4977-8047-0559bb16f78d","year":2016},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:318ac0e3ec24e74c107c7ad02a14dee6f9dfc4125260502cce519663dc8996bc","observation_id":"97529ad3-d764-4231-8a76-0374c308b18d","resolution":{"observed_at":"2026-07-05T05:50:44.690065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-07-05T05:50:44.686827Z","title":"The multimodal brain tumor image segmentation benchmark (brats).IEEE transactions on medical imaging, 34(10):1993–2024, 2014","venue":null,"work_id":"d49bd01c-f3b8-4f78-9788-cd6f4494c64a","year":1993},"citing_paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-03T15:57:58.162934Z"},"links":{"citing_paper":"/paper/2607.02051"},"observation_digest":"sha256:d25d0e8375c493f2bd62e08faa8417568d172ed451d83850b0faa63b157cbd5f","observation_id":"1fe46ce8-783b-48a8-a195-3eb66cfc36c4","resolution":{"observed_at":"2026-07-05T05:50:44.688257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.02051","last_updated":"2026-07-02T11:26:11Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T19:37:08.362404Z","submitted_at":"2026-07-02T11:26:11Z","title":"Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":1,"verified_fuzzy":39},"total_outbound_references":42},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2607.02051."}