{"as_of":"2026-08-08T03:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:26980cee81349734b584ff94ccfa526177a546d1e8aa7e8f351d7deee4345999","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:24:37.671988Z","state":"measured"},{"denominator":63,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":63,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2505.24567/citation-record","integrity":"/paper/2505.24567/integrity","json":"/paper/2505.24567/citation-record.json","paper":"/paper/2505.24567"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:43.826017Z","title":"Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,","venue":null,"work_id":"2725ef40-6b50-429c-a751-ea2ef5a3d54e","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.037292Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:445aa5c302478b3d6039a52b991d367660de4b7c0fcbd433eb5f261d53aa8abd","observation_id":"d84d5d63-750a-4814-8cd0-b9093aff6c65","resolution":{"observed_at":"2026-08-07T12:24:43.882485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.718125Z","title":"Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,","venue":null,"work_id":"16a20627-a141-4a31-ba3c-3ffa11ba3bb3","year":2019},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.094073Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:67fe48f99a6e0a0f014e7f8baa9900a935ae2059d0e974ab441c4ccd79024963","observation_id":"14f4cfa5-5d75-47b4-b1e4-d0354627aad9","resolution":{"observed_at":"2026-08-07T12:24:43.765872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.642411Z","title":"Inf-net: Automatic covid-19 lung infection segmentation from ct images,","venue":null,"work_id":"34e3203a-e1d3-44af-aa5d-3e507fc574d5","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.162108Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:3c494ec10b43f86ac00c6eaecc2ca3bcd263cb3ee1ff2a789cc0d63053b4a4cd","observation_id":"25b37b28-c2ae-46e7-9253-0eacb5ccadb4","resolution":{"observed_at":"2026-08-07T12:24:43.665725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.552521Z","title":"Transformation-consistent self-ensembling model for semisupervised medical image segmentation,","venue":null,"work_id":"7fde856c-4703-4917-bf78-19205db1de61","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.243313Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:bc69461090f0d5cfc5d40b841a94f5e8eded5a624c27e10b44f1f6957fbcbc39","observation_id":"7818d900-4cc5-4571-950b-06963e9bb218","resolution":{"observed_at":"2026-08-07T12:24:43.599572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.440047Z","title":"Ss-tbn: A semi-supervised tri-branch network for covid-19 screening and lesion segmentation,","venue":null,"work_id":"70ff31bd-86e5-4814-8f8d-1e09ecaf256c","year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.299336Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:ea73d095efee319b193caeafc1c992a37322b36afdeb34d904fab1a5999c406f","observation_id":"89f22a33-2227-4c81-ad08-81f7e0686119","resolution":{"observed_at":"2026-08-07T12:24:43.497782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.343826Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":"7774310a-062f-4121-a0d2-7db8ab1f7869","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.383727Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:02dcb8261727c6bbc88070e6eb47ee3bc88801350b2a8c8f961563ac5b111363","observation_id":"499a7ea3-525d-47a8-bf15-047b128e7fb5","resolution":{"observed_at":"2026-08-07T12:24:43.378522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.263932Z","title":"Big data in healthcare: management, analysis and future prospects,","venue":null,"work_id":"5eb885f0-c7cf-4956-a673-c771198ab26e","year":2019},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.468116Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:48a7787fa4f55afb360694d5718fd97302aa0153011cfe3ebd4d9012e50a332c","observation_id":"c2a2b7c3-ad33-4ec2-9f36-7beb6c311948","resolution":{"observed_at":"2026-08-07T12:24:43.300321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.159517Z","title":"Opportunities and challenges in using real-world data for health care,","venue":null,"work_id":"7fe0cf9b-e5df-418a-b78f-2933f9e1a837","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.508049Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:fa912ba273c7d154887f26e69ae71e6d42c908e73995b5d7c7679fad3d8bafd9","observation_id":"8022dbd0-ec98-4b13-8ba3-1ca48579bd47","resolution":{"observed_at":"2026-08-07T12:24:43.208376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:43.030328Z","title":"Semi-supervised learning by aug- mented distribution alignment,","venue":null,"work_id":"d01a0d54-c88a-464d-9765-8ceb6045136f","year":2019},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.560008Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:17acebb632ba34a84d97d552e0db32a2b86542fded684be08e81d18a783b36b7","observation_id":"b2628b7f-3347-4d26-9e51-3c83983a2577","resolution":{"observed_at":"2026-08-07T12:24:43.092488Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.923080Z","title":"Domain adaptation for medical image analysis: a survey,","venue":null,"work_id":"8d7008df-f4d4-4e66-b823-2d3a22cd27c5","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.622387Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:b81f97cd294bdef86ae78d7d2c12569a3abe2a100488e0b64ace84dd40a7a290","observation_id":"f11afdf5-a639-417f-bb95-c967ca3d6f93","resolution":{"observed_at":"2026-08-07T12:24:42.975946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.834202Z","title":"Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,","venue":null,"work_id":"1b9ce6fa-69ef-4b0a-b2e4-a5a07e80e152","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.716085Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:2e43502b787834efd02c8369bcb61b98c5cec12e32b3317c09184a602f029e5f","observation_id":"50a72c9a-c43c-4ca7-8f97-fa4284dad871","resolution":{"observed_at":"2026-08-07T12:24:42.874072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.725363Z","title":"From source to target and back: symmetric bi-directional adaptive gan,","venue":null,"work_id":"7790ad14-5bde-4134-8365-00479aab8d26","year":2018},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.795550Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:755c5bf74f7c45ab5822ffd703b9a3565f84f0d481664600b539e8e8f7f0ce7d","observation_id":"2e5fbf67-6927-44f2-9588-36447a5b0826","resolution":{"observed_at":"2026-08-07T12:24:42.777324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.604248Z","title":"Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss,","venue":null,"work_id":"7c80f96a-b84c-49da-a39a-acc4df996c57","year":2018},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.837598Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:1a1a85f6b3c9b90b280631c4984346903433d536ffbbc1f3d4333f6ff2208233","observation_id":"f5f8aa59-988d-42f0-ae9d-153f26d4bf7a","resolution":{"observed_at":"2026-08-07T12:24:42.667772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.452873Z","title":"Ecacl: A holistic framework for semi-supervised domain adaptation,","venue":null,"work_id":"7c26e767-9dab-405f-8361-da44db310b66","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.876923Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:060fdda979975e1d629ce51cdd863b3f911c3cf1c4ea367fa6a175ba0b3777d4","observation_id":"13c4a3cf-f76f-4f0c-b512-903a009af5f5","resolution":{"observed_at":"2026-08-07T12:24:42.515937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.340856Z","title":"Attract, perturb, and explore: Learning a feature alignment network for semi-supervised domain adaptation,","venue":null,"work_id":"e7bea80e-ce0b-4db9-a829-972e14ac117a","year":null},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.929243Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:f47388652ca21549ff076397fbdef5a830b78690d06e8a19763664d19962a33d","observation_id":"11cb3ebe-562f-4334-baab-96e69f18329c","resolution":{"observed_at":"2026-08-07T12:24:42.399631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.04066","last_updated":"2022-06-28T10:25:30Z","snapshot_observed_at":"2026-08-02T16:59:50.065289Z","submitted_at":"2022-05-09T06:41:18Z","title":"Multi-level Consistency Learning for Semi-supervised Domain Adaptation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.04066","snapshot_observed_at":"2026-08-07T12:24:34.973476Z","title":"Multi-level consistency learning for semi-supervised domain adaptation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:34.973476Z"},"links":{"cited_paper":"/paper/2205.04066","citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:e1db1d3c186d4682767b7eb5811f4c77ef98a754fb13c33089fe94ccf00d4992","observation_id":"7dc1b850-7180-4608-9bc9-6ffab44b3372","resolution":{"observed_at":"2026-08-07T12:24:34.973476Z","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":"2026-08-07T12:24:42.255782Z","title":"Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,","venue":null,"work_id":"a7452366-3950-42c8-8734-5443a69e70fa","year":2019},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.031158Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:15f7749de5a87b7a7a7abaada76bcdd665d0a01026982f626a278fa09f5aa138","observation_id":"cd1e7bf1-5664-486c-a15c-c6273ad60fde","resolution":{"observed_at":"2026-08-07T12:24:42.289267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.122049Z","title":"Separated con- trastive learning for organ-at-risk and gross-tumor-volume segmentation with limited annotation,","venue":null,"work_id":"be05edce-6b4e-445c-887f-213e9c5faa77","year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.077069Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:7c84c868635f95080f93b48f620bea638c8ae34ccdc72442fd532b63d07aae0a","observation_id":"fc823396-c99d-4228-9f2d-723230d0985c","resolution":{"observed_at":"2026-08-07T12:24:42.174478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:42.016915Z","title":"Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,","venue":null,"work_id":"ac6afaf9-55eb-4c89-9e4e-4970c4053a3b","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.142076Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:2e0d07b3856e04ef152d63b342a50626f46022c82f383fb4c38e9d70d0590d2a","observation_id":"4591cb03-9690-4807-a2d3-2b3f76859aa7","resolution":{"observed_at":"2026-08-07T12:24:42.068135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:41.911102Z","title":"Constructing and exploring intermediate domains in mixed domain semi-supervised medical image segmentation,","venue":null,"work_id":"610cd3e7-6956-455f-a386-93cb557376bd","year":2024},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.179184Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:98ec097a22721bcc689b4746d2aeeae6fa23b2d0c2937d4a09f367e83a5a5294","observation_id":"cec7209b-63d9-4bd2-982b-97db1ae04691","resolution":{"observed_at":"2026-08-07T12:24:41.959891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:41.824833Z","title":"Challenges and methodologies of fully automatic whole heart segmentation: a review,","venue":null,"work_id":"d38bc393-9c97-477a-bed4-f03e0732a843","year":2013},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.241727Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:fe08355079fba8f54832757d9fc1d69e51b6a1e94cbeb94066de29ccb2f3a4f9","observation_id":"f28a9526-caba-4c73-8374-d3b401d8e2d0","resolution":{"observed_at":"2026-08-07T12:24:41.863713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:41.735071Z","title":"Shape-aware semi-supervised 3d semantic segmentation for medical images,","venue":null,"work_id":"271a2bf3-b71a-4f06-9ba7-848ea61b1b42","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.367602Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:4e2d910079518b16444baf6bd56aca089401c268f81d2053763bb455b30ca705","observation_id":"43d336ea-86dc-4a9c-a123-362a77cc65ce","resolution":{"observed_at":"2026-08-07T12:24:41.783840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:41.619110Z","title":"Semi-supervised medical image segmentation through dual-task consistency,","venue":null,"work_id":"2e60d0a4-176d-4ee6-8a6c-829d15bb251f","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.429856Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:c0a9c05dc88a51c736136ccd5d1eedec01282f9e7a6dab4b3e63f166c21557b9","observation_id":"5990abc5-4547-4570-b04c-7fc906848d83","resolution":{"observed_at":"2026-08-07T12:24:41.673774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:41.477532Z","title":"Exploring smoothness and class-separation for semi-supervised medical image segmentation,","venue":null,"work_id":"0645739b-67b4-4a84-95bf-1b2170aa7fca","year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.484223Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:2391c81e2e655444699c601577a37c0ed68be4641435be10a64affcddcc40574","observation_id":"58a90c6f-e9f8-469e-aa46-8c77dc3a8e7e","resolution":{"observed_at":"2026-08-07T12:24:41.548913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:41.371018Z","title":"Caussl: Causality- inspired semi-supervised learning for medical image segmentation,","venue":null,"work_id":"2a2dd7b0-c710-4e11-9704-2cc3bd40904b","year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.537696Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:f72a2bf9ad25bdb95276be378e08bc2acb46720940f7546b550a34c1122a29f9","observation_id":"1e9797aa-c622-40b7-b0d0-6ff07001bb24","resolution":{"observed_at":"2026-08-07T12:24:41.429285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:41.276732Z","title":"Adaptive bidirectional displace- ment for semi-supervised medical image segmentation,","venue":null,"work_id":"71177e10-4627-4f79-8d4a-50250808e8a4","year":2024},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.589124Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:194cdbfc1217f43d3dff052e1b5fd8d2d7f8e51cddda72993278e3700ecdd4be","observation_id":"e99895da-4bc0-4674-bece-057aacc86b9f","resolution":{"observed_at":"2026-08-07T12:24:41.306337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:35.618602Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.618602Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:40b53ecc7cc95021311559bc1157e9ed509f8d01d013b4ee61f52f208fa3c40c","observation_id":"d7d40978-960a-41a0-af90-2319f0e2a115","resolution":{"observed_at":"2026-08-07T12:24:35.618602Z","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":"2026-08-07T12:24:41.200414Z","title":"Customized segment anything model for medical image segmentation,","venue":null,"work_id":"fdf9aab2-9aa4-4fc1-af93-47c90e50e714","year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.682091Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:140d34e8841168212784e3353c559b1a7579c549377716c2ffefa793fcdb5d0f","observation_id":"eca55203-6a50-45f1-bdc5-a14ef2aab60f","resolution":{"observed_at":"2026-08-07T12:24:41.233860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.09148","last_updated":"2023-05-02T17:06:51Z","snapshot_observed_at":"2026-07-06T15:17:05.623407Z","submitted_at":"2023-04-18T17:38:54Z","title":"SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.09148","snapshot_observed_at":"2026-08-07T12:24:35.721574Z","title":"Sam fails to segment anything?–sam-adapter: Adapting sam in underperformed scenes: Camouflage, shadow, medical image segmentation, and more,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.721574Z"},"links":{"cited_paper":"/paper/2304.09148","citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:47a2d9be8993b886e3beb58f9cd5361df9574d2ebbd1b64bffa0d58e6652cbcc","observation_id":"7a962c6b-660b-4bf6-aca5-f92fb3cfc5f5","resolution":{"observed_at":"2026-08-07T12:24:35.721574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:24:35.790080Z","title":"Segment anything in medical images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.790080Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:d20ce17672ef9f28297e87d771ef1414549f5ecb1e72a9e9b10dcfab6c6e4ad9","observation_id":"c1cf171b-ff59-4ebe-9e46-787ecb1074f0","resolution":{"observed_at":"2026-08-07T12:24:35.790080Z","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":"2026-08-07T12:24:41.086952Z","title":"Unleashing the potential of sam for medical adaptation via hierarchical decoding,","venue":null,"work_id":"513dcda2-68fa-4c83-bb59-ff31f54579fe","year":2024},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.854728Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:ce9dc381fcaedd419ac75b3352a48c028b731ede3bd294eb4be8f5d0480f8c30","observation_id":"ed2793ca-1f4c-4f2f-8835-e0ddf6b9a12f","resolution":{"observed_at":"2026-08-07T12:24:41.146203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.986107Z","title":"Adversar- ial image synthesis for unpaired multi-modal cardiac data,","venue":null,"work_id":"a8d92a16-ca74-4fe7-a735-abe6d459e5a7","year":2017},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.912426Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:a986aae0f3c8270932520e5e06ef943c4190713ee1a472c560dec1adbb4ad37c","observation_id":"b20ffddc-a482-4eb7-b4bb-4f0c143ec0b6","resolution":{"observed_at":"2026-08-07T12:24:41.031289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:35.971633Z","title":"Domain-adversarial training of neural networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:35.971633Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:6282bc9b38d6bc236279c5d99f71ccda09e2ffb07a734b140361d414d0cbe88b","observation_id":"4694f5a7-7c8f-415e-b9aa-133e531d88bd","resolution":{"observed_at":"2026-08-07T12:24:35.971633Z","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":"2026-08-07T12:24:40.901032Z","title":"Le- uda: Label-efficient unsupervised domain adaptation for medical image segmentation,","venue":null,"work_id":"3f755e5c-a7b1-4757-910e-973ef4097300","year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.029815Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:4f6f85022ffe4fe56d3e0aa4d2f42018e8134ea8017040d60b74c6a06066bb02","observation_id":"0bc8fd2f-38f2-49e3-b901-53240a4453eb","resolution":{"observed_at":"2026-08-07T12:24:40.924322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.796537Z","title":"Learning to adapt structured output space for semantic segmentation,","venue":null,"work_id":"940d06fe-04bc-47d8-82b5-f532aa88df7b","year":2018},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.093088Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:0db6eafb6ec242b6604e567564db10ce3842b90d3d39cbb16734b966e179fd3f","observation_id":"bec0fa88-ad00-4780-aef1-1ec47d4bb58f","resolution":{"observed_at":"2026-08-07T12:24:40.866001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.694206Z","title":"Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation,","venue":null,"work_id":"ee5751f4-d71d-432f-8688-314707b90829","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.133922Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:eb443deed0a835dc0605b1cc4eee0942605ed16d8c8564aba23380a71dabc9ec","observation_id":"e0d1f956-52f3-4bb6-8276-79049c880221","resolution":{"observed_at":"2026-08-07T12:24:40.755501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.603350Z","title":"Col- laborative unsupervised domain adaptation for medical image diagnosis,","venue":null,"work_id":"0dc7c0ff-e471-4268-ac7f-8beeb7bdc8a4","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.187360Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:087f6dea7d1d0801ae775ffe90606a7be27fd5eb7905f1001dcc0dbe83c68653","observation_id":"25f2b91e-1fb5-4481-9975-642fca2bcf09","resolution":{"observed_at":"2026-08-07T12:24:40.646383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.512369Z","title":"De- liberated domain bridging for domain adaptive semantic segmentation,","venue":null,"work_id":"fbc7715a-7dcb-4b19-bf19-bb6fc7690585","year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.251164Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:dae50fd542ca3206da7afdca0ec6736e4932fa21ec79e736663ca5cba6586fc1","observation_id":"47cee455-1d04-4d54-9832-0caa7f0a1d46","resolution":{"observed_at":"2026-08-07T12:24:40.546705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.420342Z","title":"Fda: Fourier domain adaptation for semantic segmentation,","venue":null,"work_id":"566b6a91-fa09-4afd-8d59-9e4228ac5d4b","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.303981Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:c870f00a0ec5fffa96a5fa3326c409084c422f407728bc58532c21c911013655","observation_id":"ddd5ae13-702d-42c2-9804-636fdea68b9f","resolution":{"observed_at":"2026-08-07T12:24:40.455282Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.328897Z","title":"Tent: Fully test-time adaptation by entropy minimization,","venue":null,"work_id":"b9979761-a3b4-4c3d-84d6-808a32ba738c","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.375567Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:cc9f3ed030c211736d373964d38706eafe0350b06579075c11317d713220e00b","observation_id":"e29f6956-6ad5-408b-974a-f6de4514f50f","resolution":{"observed_at":"2026-08-07T12:24:40.370287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:40.237750Z","title":"Each test image deserves a specific prompt: Continual test-time adaptation for 2d medical image segmentation,","venue":null,"work_id":"559d0187-04eb-4da6-a28e-080f9635a2c0","year":2024},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.426871Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:2f3290859088492b9afbdaefd271b017a0423196af800162160716c9fc93564e","observation_id":"55a8d4f9-1e0d-4a00-979b-84a2c2b6ebc4","resolution":{"observed_at":"2026-08-07T12:24:40.260336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13381","last_updated":"2023-02-24T15:56:45Z","snapshot_observed_at":"2026-07-06T14:46:23.137171Z","submitted_at":"2023-01-31T03:06:47Z","title":"When Source-Free Domain Adaptation Meets Learning with Noisy Labels","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.13381","snapshot_observed_at":"2026-08-07T12:24:36.481851Z","title":"When source-free domain adaptation meets learning with noisy labels,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.481851Z"},"links":{"cited_paper":"/paper/2301.13381","citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:b8be1170164f7ef3b2640e7dc9f6cc41a39d72bdca585a2a9c07cbfb3692feef","observation_id":"fca863fd-d3c2-4b5a-be71-b93edbd22f8e","resolution":{"observed_at":"2026-08-07T12:24:36.481851Z","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":"2026-08-07T12:24:40.109230Z","title":"Improving semi-supervised domain adaptation using effective target selection and semantics,","venue":null,"work_id":"8b5eeef3-a26d-4a86-8c72-719853d1b4cc","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.539583Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:dcd945d7b038c641e0f19b1faccfa3b279b7aeda1357f4c531b7792ee498c5a7","observation_id":"39497bc1-2197-4308-b4df-cbb235e108af","resolution":{"observed_at":"2026-08-07T12:24:40.176295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.983475Z","title":"Contradictory structure learning for semi-supervised domain adaptation,","venue":null,"work_id":"231406dc-5735-4cc3-8dc0-0cc2e3c65db9","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.589120Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:c5be571560a0e263db549b3e93defd6579f7033f77317c2115bd0785719eb201","observation_id":"70b825e1-1c08-4fc8-9586-b4fb4d9a4a09","resolution":{"observed_at":"2026-08-07T12:24:40.054004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.863614Z","title":"Bidirectional adversarial training for semi-supervised domain adaptation","venue":null,"work_id":"5275f85c-1c9c-4cbb-8124-a96a435d260d","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.674246Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:e51fd56f609dacbe33176f1c5c459ae3db9a70d74125da2cfe1f30c65403b7f8","observation_id":"94971c0a-82ac-418c-9851-177e7b2a209d","resolution":{"observed_at":"2026-08-07T12:24:39.915378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.02693","last_updated":"2023-12-22T05:39:11Z","snapshot_observed_at":"2026-08-03T21:56:13.394483Z","submitted_at":"2023-05-04T10:09:30Z","title":"Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning","version":3},"cited_work":{"arxiv_id":"2305.02693","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.02693","snapshot_observed_at":"2026-08-07T12:24:37.771922Z","title":"Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning","venue":"cs.CV","work_id":"18001d05-b8e9-4a6a-974d-7480672fb011","year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.750201Z"},"links":{"cited_paper":"/paper/2305.02693","citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:0e9e675a471e9ece05cce0e925498a33c28e48a95735f3d3b5cd317f53343127","observation_id":"855f516e-6940-4851-9400-abc8be050b9d","resolution":{"observed_at":"2026-08-07T12:24:37.828113Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.741022Z","title":"mixup: Beyond empirical risk minimization,","venue":null,"work_id":"18b482d0-ddb5-4823-82b1-da6a9a690f09","year":2017},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.812984Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:82acfdb924966bfbcfb88914b9e8494a6ac0fff51b2c8b076ff3e1f3e96554c5","observation_id":"69c74a13-1e7c-45f8-a477-280957abb9f2","resolution":{"observed_at":"2026-08-07T12:24:39.794041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.613049Z","title":"Bidirectional copy-paste for semi-supervised medical image segmentation,","venue":null,"work_id":"6724fbe4-2aa2-4917-8d85-bbcd39ba0af6","year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.905870Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:0061f4be25507ae8b59eced10c9d55d2e95b2bd4f788ad583949569304b09668","observation_id":"dd862dda-d832-479e-8db5-4346efb076ce","resolution":{"observed_at":"2026-08-07T12:24:39.670232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.493883Z","title":"Ucc: Uncertainty guided cross- head co-training for semi-supervised semantic segmentation,","venue":null,"work_id":"6161d59a-a5b7-4c77-9019-ca07b97123f6","year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:36.959998Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:ddd46881f369d421ad989d37759d53d19423b520a82c047d9cde39dc4fbbf42e","observation_id":"f78027ee-54b6-4158-8b73-b0d04ed43171","resolution":{"observed_at":"2026-08-07T12:24:39.536464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.341409Z","title":"Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,","venue":null,"work_id":"e85053ca-bcd7-4fae-ae93-e64887b63168","year":2017},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.005626Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:b9a3f8871ca2ad80aca49e38e3313afa51be3e96f486a8f627065d6b8e46736a","observation_id":"043bb393-8eca-47ff-bb82-6b9c756dbf4e","resolution":{"observed_at":"2026-08-07T12:24:39.432225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.228641Z","title":"Fixmatch: Simplifying semi- supervised learning with consistency and confidence,","venue":null,"work_id":"25d39825-1564-42c2-9cf1-de557f09755f","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.038746Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:286eee25bbb3fa0e2dc695847e8054054fb6a65b18a49214ef13c96e3a0d8bc5","observation_id":"09591f05-66b4-4d8d-8c94-9f0c4a403696","resolution":{"observed_at":"2026-08-07T12:24:39.289490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:39.119037Z","title":"Fixmatchseg: Fixing fixmatch for semi- supervised semantic segmentation,","venue":null,"work_id":"c9ca8c04-3174-44e1-af13-f3dcb4e3bc30","year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.070508Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:b064dc1b02e8876ef91de3a599a3bcbb1890cd994af17d88683ef1a365f617aa","observation_id":"117fd21c-ac0e-4aa3-a90d-8b36ae2a810f","resolution":{"observed_at":"2026-08-07T12:24:39.174048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.975911Z","title":"Revisiting weak-to- strong consistency in semi-supervised semantic segmentation,","venue":null,"work_id":"e2bd2d64-809f-4776-abb2-554e9317ba5f","year":2023},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.115002Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:08da18b82d95911ddc27f4d68d7bff28501a0dd68977d7025a6b34222397d95f","observation_id":"b31c7c54-4399-4d19-8e93-1b6e89c8b67b","resolution":{"observed_at":"2026-08-07T12:24:39.060482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.832606Z","title":"Bootstrap your own latent-a new approach to self-supervised learning,","venue":null,"work_id":"915584b6-b79e-40c2-b962-9161140feb4c","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.164001Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:cbad78c0ca427c1edd308180bd928f244749d8de0b9e785929c03ff2aa4c9019","observation_id":"86d9f09c-25e5-453b-8fbe-18223f5a1161","resolution":{"observed_at":"2026-08-07T12:24:38.888226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.707160Z","title":"Instance credibility inference for few-shot learning,","venue":null,"work_id":"a1197c20-c719-4d32-9023-91cd256461b6","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.205811Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:12da912c197d0d2f8878f5bf4295fd337a7eec8625e4debbb98951a50fccf1fc","observation_id":"175ab655-519c-4ee2-98dd-f2b1c3f8e669","resolution":{"observed_at":"2026-08-07T12:24:38.770214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:37.266684Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.266684Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:449d6b6ada1c22b24c4862178c3f7106acb317ca68f7e3fd4c687a89d9b7d8be","observation_id":"6616f166-f289-441e-a3cf-794cb944e197","resolution":{"observed_at":"2026-08-07T12:24:37.266684Z","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":"2026-08-07T12:24:38.599268Z","title":"Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets,","venue":null,"work_id":"c16de0ae-2798-49ee-9d81-b909017f4a9f","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.319504Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:e62725a9499aa9781cb1213081a2a39793576c17e6caf284904a6b314a86aab1","observation_id":"ef10cce1-1a06-4434-81f8-8fb8454a090e","resolution":{"observed_at":"2026-08-07T12:24:38.645026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.503781Z","title":"Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge,","venue":null,"work_id":"269653a1-349c-48e8-b972-6d206adff201","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.361253Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:0d15036c15ac6518def898bcb5da1de20b093f8b24778045984fd1cbdc25ab51","observation_id":"eafea23f-ff66-4715-86ef-e80c240cd7f1","resolution":{"observed_at":"2026-08-07T12:24:38.546288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.330853Z","title":"Dataset of breast ultrasound images,","venue":null,"work_id":"fda652c5-7a15-4af3-9707-8f1b03e2b448","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.419531Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:e4942ec35f1db3136a69f5c6e2a7d99b55ab4fe54a8f133571fcb07c42350502","observation_id":"37ab5183-afb7-47cb-9d1f-9aecb7eedb97","resolution":{"observed_at":"2026-08-07T12:24:38.408155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.226537Z","title":"Unsupervised domain adaptation for cardiac segmentation: Towards structure mutual information maximiza- tion,","venue":null,"work_id":"5af959fe-274c-486b-b65f-e05566c11cb1","year":2022},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.490588Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:a543269e70520cc4a93fdf904215261b457f307a932407b2258ac2f8e76ded65","observation_id":"0a407c31-33cb-4222-aad4-8d254f48fd7b","resolution":{"observed_at":"2026-08-07T12:24:38.267065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.120430Z","title":"Classmix: Segmentation-based data augmentation for semi-supervised learning,","venue":null,"work_id":"1122e51c-9f93-42f8-a3c4-924971a365da","year":2021},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.547640Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:cc3e89b6cc23d0c6b9eeb6fa28f6dfcad64b1d58b05592f005fd924ce602f69d","observation_id":"fe0be427-d93c-4127-96bd-576584a3be6a","resolution":{"observed_at":"2026-08-07T12:24:38.183413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:38.040779Z","title":"Milking cowmask for semi- supervised image classification,","venue":null,"work_id":"5fcbae5e-dd08-40a3-89e9-f2b2aa7dc8db","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.606221Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:c3756ec125a2bd005d0147e30394433425e44a2760c04c5c9d50328e34eabeae","observation_id":"af918cdd-a327-4adf-a084-952a472b4e62","resolution":{"observed_at":"2026-08-07T12:24:38.079325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T12:24:37.918328Z","title":"Fmix: Enhancing mixed sample data augmentation,","venue":null,"work_id":"2e90afca-8f20-4926-adb6-239e30f2f805","year":2020},"citing_paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T12:24:37.671988Z"},"links":{"citing_paper":"/paper/2505.24567"},"observation_digest":"sha256:5dd6fda87c033390266622493c31b2ed9c4f76d80f643f7e618439907e622c08","observation_id":"db5268a6-c75b-427f-861c-c8550088312e","resolution":{"observed_at":"2026-08-07T12:24:37.970492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.24567","last_updated":"2025-05-30T13:21:05Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T12:15:51.294187Z","submitted_at":"2025-05-30T13:21:05Z","title":"Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":1,"verified_fuzzy":55},"total_outbound_references":63},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2505.24567."}