{"as_of":"2026-08-15T04:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e7222694e074e921915088ce2dd18c386f220e8dd46a98d987b6848db4976c8e","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:56:18.197904Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2506.23038/citation-record","integrity":"/paper/2506.23038/integrity","json":"/paper/2506.23038/citation-record.json","paper":"/paper/2506.23038"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:56:25.621765Z","title":"Accessed: 2021-09-06","venue":null,"work_id":"b5c67726-6ffe-4587-b939-a8a46ac7780f","year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.027435Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:527986c189a4eaa62d47422d5d25fa2c130108d5c93f1d596ac97aeefff931c1","observation_id":"7fd564c4-9ea1-4175-a6d7-ab0d2c51f050","resolution":{"observed_at":"2026-08-06T21:56:25.696220Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.04802","last_updated":"2023-01-12T04:22:23Z","snapshot_observed_at":"2026-08-13T13:05:35.145811Z","submitted_at":"2023-01-12T04:22:23Z","title":"Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.04802","snapshot_observed_at":"2026-08-06T21:56:14.158714Z","title":"Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.158714Z"},"links":{"cited_paper":"/paper/2301.04802","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:5324260ff60c8f56452d11c4495f8652b02f11f545947196df8a33b6a588e374","observation_id":"da92f133-4d66-4c8c-ab8b-74228ffb257c","resolution":{"observed_at":"2026-08-06T21:56:14.158714Z","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":"2026-08-06T21:56:25.533697Z","title":"The medical segmentation decathlon.Nature communications, 13(1):1–13, 2022","venue":null,"work_id":"fbe05538-b10c-4de9-a1ad-5079e78d7510","year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.281398Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:79bfb398f89ba4d1b58843d6c633cf728d3c329439d3ccaaacaa80531a746ed4","observation_id":"2865b3f3-bc29-4718-b06a-4351b4d21d61","resolution":{"observed_at":"2026-08-06T21:56:25.581723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:25.353750Z","title":"Bidirectional copy-paste for semi-supervised medical image segmentation","venue":null,"work_id":"f6fc7ac4-8d57-47b4-ac03-8880911b7659","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.378184Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:e2845149cb47ee556cadb81b6ea77b272ccad2c05d7b2c936c4386da508792f2","observation_id":"de63e473-08fc-4b8a-b2c3-444775e2559f","resolution":{"observed_at":"2026-08-06T21:56:25.447278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:25.150948Z","title":"Red blood cell image generation for data augmentation using con- ditional generative adversarial networks","venue":null,"work_id":"b579ff8b-417a-4164-829b-a540185e731c","year":2019},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.502406Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:23ce31678a8e0c374cf7f4d29bad46d6c8158f043db2cc85168b1c19c138235c","observation_id":"946b176b-ea81-4a31-a680-14372f76bf7b","resolution":{"observed_at":"2026-08-06T21:56:25.248212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:24.910066Z","title":"Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?IEEE transactions on medical imaging, 37(11):2514–2525, 2018","venue":null,"work_id":"528fc041-4039-4401-97b1-80932b8cdfeb","year":2018},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.637024Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:e8a5986975b1579c4ef29e41435ceeefd62d40c801e68a6841c1b41b3d29b80c","observation_id":"d8d74161-0fef-41e4-9267-5c49107ffc46","resolution":{"observed_at":"2026-08-06T21:56:25.006492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:24.682550Z","title":"Extracting training data from diffu- sion models","venue":null,"work_id":"1c1664cd-6feb-4a8a-9c6d-ac8a4d7f1e74","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.731323Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:f9e4e869fb9506532f822ab5db22cf00a9235af3bbb32c302dd117117351af1d","observation_id":"167d41c9-d039-4502-a9c9-3750fa196c66","resolution":{"observed_at":"2026-08-06T21:56:24.796617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:24.439390Z","title":null,"venue":null,"work_id":"cf2524cd-02ab-40df-a424-469a4e5c25f1","year":2020},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.812013Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:f1bb8afa710dead5e0a946a11ac8589e58459fe1c96f3a890fa2f25480edce99","observation_id":"fe6da545-767e-4071-ba1c-329453557463","resolution":{"observed_at":"2026-08-06T21:56:24.550312Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:24.249483Z","title":"Semi-supervised task-driven data augmentation for medical image segmentation.Medical Image Analysis, 68:101934, 2021","venue":null,"work_id":"54499e9c-01e1-4470-9152-5dda1109a4c2","year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:14.947713Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:e680791c9e54cd7230159a3e43fb756bb818dd799ccb53d7d6e69bfae7f83ef6","observation_id":"13176685-9101-485a-bfe9-87bc1ed11b71","resolution":{"observed_at":"2026-08-06T21:56:24.327627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04306","last_updated":"2021-02-08T16:10:50Z","snapshot_observed_at":"2026-08-14T08:44:08.583921Z","submitted_at":"2021-02-08T16:10:50Z","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04306","snapshot_observed_at":"2026-08-06T21:56:15.026552Z","title":"Transunet: Transformers make strong encoders for medi- cal image segmentation.arXiv preprint arXiv:2102.04306,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.026552Z"},"links":{"cited_paper":"/paper/2102.04306","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:504000b40ccb680d126d96e11c5503af454f411a8c6aedfc2214dc710a531f9d","observation_id":"ca4e190a-0060-4f43-9611-cd7853131ac0","resolution":{"observed_at":"2026-08-06T21:56:15.026552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-07T13:44:53.690521Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-06T21:56:15.113543Z","title":"Rethinking atrous convolution for seman- tic image segmentation.arXiv preprint arXiv:1706.05587,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.113543Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:b31d18bb361e0c98a2d5468de0dddf65dbff03e47b83e4db2759351ef5e0480f","observation_id":"f1b8169b-c0f7-4da9-9d63-57b758a1ae9f","resolution":{"observed_at":"2026-08-06T21:56:15.113543Z","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":"2026-08-06T21:56:15.177475Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.177475Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:121b415aa99764d6593c746e4dd75b102839b0e01b0e46c24c2333258a5cc0a6","observation_id":"53f5ed65-6525-4982-bdb1-a84caa02cd6d","resolution":{"observed_at":"2026-08-06T21:56:15.177475Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.03368","last_updated":"2019-03-29T17:36:27Z","snapshot_observed_at":"2026-08-14T17:18:50.675083Z","submitted_at":"2019-02-09T04:18:10Z","title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.03368","snapshot_observed_at":"2026-08-06T21:56:15.235656Z","title":"Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the interna- tional skin imaging collaboration (isic).arXiv preprint arXiv:1902.03368, 2019","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.235656Z"},"links":{"cited_paper":"/paper/1902.03368","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:43c627f9bd657011288a3872185fe98b07a0c18253ad7d73f3e1d0c77da9de0a","observation_id":"6c49a700-03f7-4461-ac06-fdcb10e8057e","resolution":{"observed_at":"2026-08-06T21:56:15.235656Z","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":"2026-08-06T21:56:15.283905Z","title":"Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.283905Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:e5b48f47617233c623f2de9663872091014b4ff4b82d23262263a68b29d9b47c","observation_id":"2293fc89-a668-492a-bb74-9a6d41ec6e11","resolution":{"observed_at":"2026-08-06T21:56:15.283905Z","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":"2026-08-06T21:56:24.005835Z","title":"Arsdm: Colonoscopy images synthesis with adaptive refinement se- mantic diffusion models","venue":null,"work_id":"53515839-7b35-4bb5-baba-f17d322f3463","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.333406Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:70678c6481ff5319db3b6404fb7170e2c53aa6d4a89a27a06d869e1ae5c5719e","observation_id":"609f8c43-6aa7-423c-bf71-a1cf90778f60","resolution":{"observed_at":"2026-08-06T21:56:24.120899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:23.749266Z","title":"Can segmentation models be trained with fully synthetically generated data? InInter- national Workshop on Simulation and Synthesis in Medical Imaging, pages 79–90","venue":null,"work_id":"afe404d1-1edc-43ce-8997-411598d00066","year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.385422Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:b8950ddbcd5dc7c671637b8a8579ebf460cebb1afbf05d16a2d454cc6058eb8c","observation_id":"21983463-6f3d-4c5c-8dda-43acde03baab","resolution":{"observed_at":"2026-08-06T21:56:23.876225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:23.557502Z","title":"Medgen3d: A deep generative framework for paired 3d image and mask generation","venue":null,"work_id":"8691aea6-1f40-40df-aecf-31377d317470","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.444699Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:7f8c72f8c80ef2f51097f4b0d0d56389d7eca46a5c6ba2f5b34b8e28dbb569fc","observation_id":"12c22ca7-5a29-46dd-845f-8f79be60f9d9","resolution":{"observed_at":"2026-08-06T21:56:23.649983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:23.358337Z","title":"Unetr: Transformers for 3d medical image segmentation","venue":null,"work_id":"c287ccb8-6f30-4d25-9170-99dd0bec2be9","year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.499505Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:4d1c171834396c25b9a82f7beb6df6fb3495b46ff94e36fcf2a019b0ac638845","observation_id":"c81b914e-0581-401d-aa8b-81b5f926b7c7","resolution":{"observed_at":"2026-08-06T21:56:23.450814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07574","last_updated":"2023-02-15T06:26:38Z","snapshot_observed_at":"2026-08-13T14:05:20.349977Z","submitted_at":"2022-10-14T06:54:24Z","title":"Is synthetic data from generative models ready for image recognition?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07574","snapshot_observed_at":"2026-08-06T21:56:15.570823Z","title":"Is synthetic data from generative models ready for image recognition? arXiv preprint arXiv:2210.07574, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.570823Z"},"links":{"cited_paper":"/paper/2210.07574","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:95db155aaea89989f6ccaa2de0d61214bb069755158add2bd9a90d483038edfc","observation_id":"20a68a1d-e3a2-4eb3-a58f-49d96ec5a5d5","resolution":{"observed_at":"2026-08-06T21:56:15.570823Z","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":"2026-08-06T21:56:15.643391Z","title":"Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.643391Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:7c142beda680d5cafe191eb6c043712d041a187b11cf53fa0390a20bc525e863","observation_id":"31373d75-e68d-4371-9089-d25bd31b5d94","resolution":{"observed_at":"2026-08-06T21:56:15.643391Z","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":"2026-08-06T21:56:23.118346Z","title":"Semi- supervised contrastive learning for label-efficient medical image segmentation","venue":null,"work_id":"30875db3-3b0c-454f-a19d-56a35830371a","year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.679202Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:2469ab3be93fce6d72fd374a8edf93f6385faf60e03c428fede2b440b487ba91","observation_id":"a1086405-69ba-497f-9da3-f14d3ef56a2e","resolution":{"observed_at":"2026-08-06T21:56:23.240041Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:22.907921Z","title":"Deep learning approaches for data augmentation in medical imaging: A review.Journal of Imaging, 9(4):81, 2023","venue":null,"work_id":"b7bc73b5-d3c7-4fe1-87ab-b5ba98382f2c","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.812481Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:9b16a94fb9018ba10c0592ca428e58486a108ebd7c431cbce4fb5a081911d7b5","observation_id":"70348f0c-be8c-4efe-b174-b96128acdc8e","resolution":{"observed_at":"2026-08-06T21:56:22.974013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.03364","last_updated":"2023-01-03T11:05:41Z","snapshot_observed_at":"2026-08-13T13:48:23.305720Z","submitted_at":"2022-11-07T08:37:48Z","title":"Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.03364","snapshot_observed_at":"2026-08-06T21:56:15.889684Z","title":"Medical diffusion– denoising diffusion probabilistic models for 3d medical im- age generation.arXiv preprint arXiv:2211.03364, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.889684Z"},"links":{"cited_paper":"/paper/2211.03364","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:e01d9f84223eff19fa64cecd282d60b36ec35281cefee538e7ac33393f8441ec","observation_id":"72969fb5-f599-48d1-9c48-60fb9cef451e","resolution":{"observed_at":"2026-08-06T21:56:15.889684Z","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":"2026-08-06T21:56:22.737342Z","title":"Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization","venue":null,"work_id":"309f8d00-e811-4409-97f9-320a0e8328f5","year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:15.959864Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:f1c631d6b90057306de00d2a957762892a277f56de847fc50f758f583839fb49","observation_id":"ca769095-e2a5-48ea-8e1c-fb97afd77bc1","resolution":{"observed_at":"2026-08-06T21:56:22.835027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:22.544769Z","title":"Repaint: Inpainting using denoising diffusion probabilistic models","venue":null,"work_id":"ce9a604b-6e10-427b-9222-eb1c4ad6605e","year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.031564Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:d53ae985c338ad8310b61270deb0f8266bbc2838485f5bf4afaa62567e5cf4bb","observation_id":"a930dd78-3c25-4250-a740-eac86cd8fba3","resolution":{"observed_at":"2026-08-06T21:56:22.630217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:22.334141Z","title":"Semi-supervised medical image segmen- tation via cross teaching between cnn and transformer","venue":null,"work_id":"20d35f50-b240-425b-819e-c81b52390dc1","year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.088817Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:ecf782933bcf747adf3957e13e0eb9b9b51312c74064e498a41877834a7f1138","observation_id":"1bc54af4-ddcd-403a-924d-9002e3370bb3","resolution":{"observed_at":"2026-08-06T21:56:22.442966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:22.155099Z","title":"Learning data aug- mentation for brain tumor segmentation with coarse-to-fine generative adversarial networks","venue":null,"work_id":"3368f5f0-ac23-4c1f-891e-5153c53a0b4a","year":2018},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.128891Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:693de2bfe29c9db34dc56192d295dbc4c0f4f3cbb4c35d57b3bdfdf77219e2d9","observation_id":"54783a63-a849-49f0-965e-f273373fc83a","resolution":{"observed_at":"2026-08-06T21:56:22.224695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:21.989651Z","title":"Data augmentation for brain-tumor segmentation: a review","venue":null,"work_id":"0f0ee4e4-09cb-4500-98b9-b940e0db769c","year":2019},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.187275Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:f42a57f5dbc475b2a63e112f001821b1275ffcb1f615c57ccd4c2892e2f36f4d","observation_id":"093cec22-6693-4929-b12d-77fa8a303534","resolution":{"observed_at":"2026-08-06T21:56:22.068416Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:21.815093Z","title":"Generative adversarial network based synthesis for supervised medical image segmentation","venue":null,"work_id":"0a3533c4-1da8-4537-a56f-27fd8608b3e0","year":2017},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.258079Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:767e11405ae581a57d069887d1958dedec8abdf026df5909b69f2f065fb24bc1","observation_id":"1952fe53-5229-468c-8511-27026e9a2a16","resolution":{"observed_at":"2026-08-06T21:56:21.905953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.14303","last_updated":"2023-11-13T05:11:52Z","snapshot_observed_at":"2026-08-13T10:05:20.526233Z","submitted_at":"2023-09-25T17:19:26Z","title":"Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation","version":4},"cited_work":{"arxiv_id":"2309.14303","doi":null,"metadata_source":"pith","pith_arxiv_id":"2309.14303","snapshot_observed_at":"2026-08-06T21:56:18.342036Z","title":"Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation","venue":"cs.CV","work_id":"5f5cd49b-c4e6-40e9-862c-8e879423072c","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.337016Z"},"links":{"cited_paper":"/paper/2309.14303","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:38fe14a1148ac31e2ca1e6327e89c80dd86da10b91d61bb14f5fc4ddc118368d","observation_id":"e64d876a-c651-43f4-adcf-20e5ede6babd","resolution":{"observed_at":"2026-08-06T21:56:18.383434Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:21.661420Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":"6f6c8682-b3db-498b-8572-8b7df1a25a62","year":null},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.402054Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:c9e32c1bec3ea0ea46ae45fe0cf33b521cceb7b339a1f289ec684c3547f13349","observation_id":"3ce9386b-47d1-4c7b-8ee2-dce2f6413e8b","resolution":{"observed_at":"2026-08-06T21:56:21.731835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:21.537078Z","title":"Un- supervised medical image translation with adversarial diffu- sion models.IEEE Transactions on Medical Imaging, 2023","venue":null,"work_id":"ef10b9fd-f18c-44f9-b134-79e2d96f917e","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.463729Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:c210c9694608a8678bbbe959b0e4321903210ff1db85d9178fe7ee037472b07e","observation_id":"52a52456-15ed-4a22-8c4e-7a52cced9209","resolution":{"observed_at":"2026-08-06T21:56:21.585184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:21.383388Z","title":"Self-paced contrastive learning for semi-supervised medical image segmentation with meta- labels.Advances in Neural Information Processing Systems, 34:16686–16699, 2021","venue":null,"work_id":"9067fbf2-cc31-49d1-ace9-e7295ac78720","year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.520332Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:8624e1d036bf53c0d3a202b9bec52f9f0136dcf448305bd95d134b77ab95cd22","observation_id":"73e57e59-f5fc-4ba0-8d73-eacb67fa6549","resolution":{"observed_at":"2026-08-06T21:56:21.451348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:21.166425Z","title":"Brain imaging generation with latent diffusion models","venue":null,"work_id":"ee3f7a4f-e3aa-4723-8200-7592d1251a3e","year":null},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.580793Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:64ec39db889f637500e6f66f71a2a5275678917702e753937d3111c3859006f4","observation_id":"818e7734-a01a-43da-8873-897dc3732cd5","resolution":{"observed_at":"2026-08-06T21:56:21.251393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:16.666634Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.666634Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:0d31c33060075bef91eaf074a283af6abb0c8de5b4db9f0f64a813b71eee0e60","observation_id":"671a3286-9bfe-4412-8731-2a599221884a","resolution":{"observed_at":"2026-08-06T21:56:16.666634Z","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":"2026-08-06T21:56:20.924903Z","title":"Palette: Image-to-image diffusion models","venue":null,"work_id":"68f1d9cf-6cf2-4e76-8f2b-cb70d24d0d0d","year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.769972Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:3c740709e0da665c0897018b4a20a378658eb18e90f6806e56f6aa19cbd3b3dd","observation_id":"e2e6f99e-eeef-4dcc-9580-c7dbc3d32fc6","resolution":{"observed_at":"2026-08-06T21:56:21.035534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:20.668654Z","title":"A novel data augmentation method using style-based gan for robust pul- monary nodule segmentation","venue":null,"work_id":"7e701622-2357-46b7-8917-28b58fbd6e00","year":null},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.838881Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:1b649b3a9f28daa4e1e1564093139f7c248f9b965fd742ce29fef52364731387","observation_id":"61f7e203-255a-4ddd-b217-457a9a999d0f","resolution":{"observed_at":"2026-08-06T21:56:20.786748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:20.518737Z","title":"Medical image synthesis for data augmentation and anonymization using generative adversarial networks","venue":null,"work_id":"577b217f-4330-4a9a-ae41-8026cf8ed7dc","year":2018},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.906813Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:1a7465b977afeac751f505457061244b7fa3948a1edc834e6d91c8b1d8213318","observation_id":"4a068a7a-6d2c-49e0-a3b4-92c1ac6e3a70","resolution":{"observed_at":"2026-08-06T21:56:20.601370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:20.260139Z","title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","venue":null,"work_id":"9fa8c48a-1f0f-42c9-b020-b8822df39362","year":2020},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:16.989935Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:df0dd0fcf1ca02fbdfdfa619ffa837cca736d9d2be012a5999809d357c5c6038","observation_id":"2ffd7e08-74e3-4041-bdec-4858b17a97aa","resolution":{"observed_at":"2026-08-06T21:56:20.403083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:20.079334Z","title":"Diffusion art or digital forgery? investigating data replication in diffusion models","venue":null,"work_id":"a6efa3f6-6045-41c2-bd41-dc15b2debf1e","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.044868Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:0e58b6d9502552d8bfb0a4abda0f5c8f4ca021dca7d6655557d34de76067f656","observation_id":"5764af69-934c-4613-ad06-4d87a29f67a1","resolution":{"observed_at":"2026-08-06T21:56:20.144807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-06T21:56:17.122566Z","title":"Denoising diffusion implicit models.arXiv preprint arXiv:2010.02502, 2020","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.122566Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:180d3aff6fd0904d50284bca68e48586ba717af307a6923c4d28de09f6787e93","observation_id":"36099b69-c7e0-4d32-a0b9-49de98c1a1ff","resolution":{"observed_at":"2026-08-06T21:56:17.122566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.07944","last_updated":"2025-06-10T20:01:59Z","snapshot_observed_at":"2026-08-13T12:49:10.941762Z","submitted_at":"2023-02-07T20:42:28Z","title":"Effective Data Augmentation With Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.07944","snapshot_observed_at":"2026-08-06T21:56:17.188764Z","title":"Effective data augmentation with diffusion models.arXiv preprint arXiv:2302.07944, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.188764Z"},"links":{"cited_paper":"/paper/2302.07944","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:6ea654d38352e281f3b44d7ed8f0965f77195c92c1c23dc1c170c8ed68f5a7eb","observation_id":"83cfe0c2-dda6-42d4-bc3c-6fc6a2102b1b","resolution":{"observed_at":"2026-08-06T21:56:17.188764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11681","last_updated":"2024-01-21T13:35:44Z","snapshot_observed_at":"2026-08-13T12:20:08.514747Z","submitted_at":"2023-03-21T08:43:15Z","title":"DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.11681","snapshot_observed_at":"2026-08-06T21:56:17.256952Z","title":"Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using dif- fusion models.arXiv preprint arXiv:2303.11681, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.256952Z"},"links":{"cited_paper":"/paper/2303.11681","citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:f144c33b697f70e4852c39cc881729cfd180772ad4b57df0ae6c233284cde503","observation_id":"f74c47f0-073f-4ea3-a5a3-2ada92fe87a4","resolution":{"observed_at":"2026-08-06T21:56:17.256952Z","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":"2026-08-06T21:56:19.920204Z","title":"Exploring smoothness and class-separation for semi-supervised medical image segmentation","venue":null,"work_id":"1b3b452d-5973-4680-aa64-a1bc5c92b16a","year":null},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.328157Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:1c89fa683331a34a813fa7be108c57e019957bcc587a99ed9a22da198bafabb6","observation_id":"e06852c2-58b1-4eb2-b4a5-afa594bc988c","resolution":{"observed_at":"2026-08-06T21:56:20.006358Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:17.387517Z","title":"Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and do- main adaptation.Medical image analysis, 65:101766, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.387517Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:bd691e9239244bb7f2dbfb737878c7300a4ad61d62687eb94f604fd8d16dad5a","observation_id":"47af1076-6d24-4a96-a74f-3736951af7cf","resolution":{"observed_at":"2026-08-06T21:56:17.387517Z","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":"2026-08-06T21:56:19.790870Z","title":"Revisiting weak-to-strong consistency in semi-supervised semantic segmentation","venue":null,"work_id":"fbc2dcf1-84d7-4564-ac0a-036f6d15015f","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.452238Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:fc7a87bde5604bf3951acfd212bbf2bdd920199ac5dad6bca39c4e6a18f53c13","observation_id":"14058f4d-d253-4d26-8dab-65241c154366","resolution":{"observed_at":"2026-08-06T21:56:19.853247Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:19.636746Z","title":"Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive dis- tillation","venue":null,"work_id":"bd7b744d-8cdc-4c11-9245-bd58b3ecd9eb","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.536701Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:32a9e06f6c4243b5cb5298b05dce07895c382b9cd0be73b0e9306803df83ef6f","observation_id":"5baa149c-c734-4732-9726-fcc9db725ae0","resolution":{"observed_at":"2026-08-06T21:56:19.730398Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:19.478861Z","title":"Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image seg- mentation","venue":null,"work_id":"93168d25-2d50-4490-b1ad-4147771d0413","year":null},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.656227Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:efd737fb83588dc3dcf8bbbdc9eeadbe463fd22c86605de1f38fe9f0f9074e4f","observation_id":"958c34e8-d1b3-467f-96a7-41c6fdc5b6fe","resolution":{"observed_at":"2026-08-06T21:56:19.542608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:19.331573Z","title":"Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation","venue":null,"work_id":"144e7333-d4ee-403b-bc0a-8b959bf941d9","year":2019},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.721473Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:b5aa671dbd2b083cbaaffbb6041062adc591199a9d223d0cea21d84d500e1c8b","observation_id":"640df4af-e34a-49fb-8b17-888405f9c8d9","resolution":{"observed_at":"2026-08-06T21:56:19.407503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:19.152469Z","title":"Diffusion-based data augmentation for nuclei image segmentation","venue":null,"work_id":"7d597b5e-4c7f-4e68-92a3-59ad1d1be2a1","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.782674Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:5dba86befec26859a43c1a76e73fae9a3f8efac1fda0d84c23541464740687be","observation_id":"cbd9df96-eb8a-4135-8749-a6970e6d7a90","resolution":{"observed_at":"2026-08-06T21:56:19.235338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:19.029383Z","title":"Positional contrastive learning for volumetric medical image segmentation","venue":null,"work_id":"f81b211a-ece1-410a-84ae-e8fea8cbea0e","year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.873408Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:7e11ffb9c5f8ac38bc8ecd2b32f98875b58100d6a4853fbe9f76ee73ec7f85b8","observation_id":"ee942948-6a30-446b-8036-23ae89803fb1","resolution":{"observed_at":"2026-08-06T21:56:19.084691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:18.875095Z","title":"Adding conditional control to text-to-image diffusion models","venue":null,"work_id":"9f2af0d2-c933-4311-aee9-f77e1e7c8293","year":2023},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:17.972379Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:9d3b491e7c7736a994c651a52eed4e1f8d2ee88bcd91add1bd7b5c4e65597027","observation_id":"3decf2cb-8d03-4c70-98b3-55a1c6f8089b","resolution":{"observed_at":"2026-08-06T21:56:18.952376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:18.769200Z","title":"Unsupervised feature clustering im- proves contrastive representation learning for medical image segmentation","venue":null,"work_id":"cebf28e9-57e6-4d68-a20f-5b2e5292fdbf","year":2022},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:18.058311Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:2eabd732e9cc1e9859987d3b885ca4f08a7bab3512b952e07754b0b146c09bc7","observation_id":"12193b63-10fd-4eb1-b42a-62e0d6ab4177","resolution":{"observed_at":"2026-08-06T21:56:18.825039Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:18.632237Z","title":"Datasetgan: Efficient labeled data factory with minimal human effort","venue":null,"work_id":"2406b264-6ba1-4984-8bd7-ed17a3c556ef","year":2021},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:18.130720Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:cd5f649c80482a23e910f0a52513d598a28b2480c894b0302308fcff98842c8c","observation_id":"0d3f86ed-568b-405e-9dce-642446657694","resolution":{"observed_at":"2026-08-06T21:56:18.704469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T21:56:18.496962Z","title":"Data augmentation using learned transformations for one-shot medical image segmentation","venue":null,"work_id":"9954c546-bea5-4e86-857d-5e534d955a6d","year":2019},"citing_paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T21:56:18.197904Z"},"links":{"citing_paper":"/paper/2506.23038"},"observation_digest":"sha256:21f17c67f5c46622345b93ed4bcc6f94425be1c057789443dfe47a3af004288f","observation_id":"ab386720-14c2-461a-aca9-cf7e2fb62c93","resolution":{"observed_at":"2026-08-06T21:56:18.565373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.23038","last_updated":"2025-06-28T23:44:18Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T18:51:37.441235Z","submitted_at":"2025-06-28T23:44:18Z","title":"Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":39},"total_outbound_references":55},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2506.23038."}