{"as_of":"2026-08-13T10:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d2aa2957dab6bf74f20f16e705c7447b52baf246cd36d1e43c009b9c6946f133","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:37:58.713765Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2507.21608/citation-record","integrity":"/paper/2507.21608/integrity","json":"/paper/2507.21608/citation-record.json","paper":"/paper/2507.21608"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:38:00.499310Z","title":"Automated human induced pluripotent stem cell colony segmen- tation for use in cell culture automation applications,","venue":null,"work_id":"293346a8-beaa-497c-9dfa-b5c47531825b","year":2023},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.188301Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:70e3f6e7ce7ec8cb4d157df1b3c8f3e728e828f54afc806c34795438cc284642","observation_id":"f31bcb35-eb03-48b8-ad6a-29e7aed06eb2","resolution":{"observed_at":"2026-08-06T12:38:00.547859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:38:00.375497Z","title":"TransUNet: Re- thinking the U-Net architecture design for medical im- age segmentation through the lens of transformers,","venue":null,"work_id":"506a8e90-3661-4517-b57c-311932667316","year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.227288Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:2b3192caea02637b775c8a10f70cb827595ddd40aa936c57e77c719c0b1c35be","observation_id":"75df4b33-6a12-41b9-bf1d-b7bc0a9659b2","resolution":{"observed_at":"2026-08-06T12:38:00.461208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:38:00.234277Z","title":"nnu-net revisited: A call for rigorous validation in 3d medical image seg- mentation,","venue":null,"work_id":"d39702e9-1aa3-4a22-8f5c-4f27e4a62938","year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.280672Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:ed7e97d6cc96d4b8d3c80262d47f3d0aa0a6454d358afdd0369eafef6dcaa5b0","observation_id":"f4fb7084-6e90-4daf-a737-7cb824b78d66","resolution":{"observed_at":"2026-08-06T12:38:00.280219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:38:00.158370Z","title":"nnU-Net: a self-configuring method for deep learning-based biomedical image seg- mentation,","venue":null,"work_id":"9a7a8c40-abc3-4172-8bde-3a6544b8f20b","year":2021},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.345638Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:8a7dbef841f25c2cefcdd21c1319fcf16b0e39ae57936ae310dad76a541efd29","observation_id":"3100a107-0413-4c27-b4a8-cd127ebc48f8","resolution":{"observed_at":"2026-08-06T12:38:00.190677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:38:00.043805Z","title":"Improving the reliability of semantic segmentation of medical images by uncertainty modeling with Bayesian deep networks and curriculum learning,","venue":null,"work_id":"b428037c-72a0-4657-adb2-d176e01ff042","year":2021},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.472652Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:283d87bb2fcea6fe01968175aa6379ca54a3b8b522ad90ca0bec225114e0277b","observation_id":"b488525c-d7d3-4f16-b297-35bc76860cd2","resolution":{"observed_at":"2026-08-06T12:38:00.083491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.959217Z","title":"Segment anything,","venue":null,"work_id":"fc716e49-be74-44e7-962b-7f3e172ae4a7","year":2023},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.605388Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:7403b1478f3116f39fe0a3a631706cf9b11becf5ea5f8fcc6d9dd51088163d49","observation_id":"7d1d887a-8a16-446b-a86b-861911785b9b","resolution":{"observed_at":"2026-08-06T12:37:59.991888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-06T12:37:57.654683Z","title":"SAM 2: Seg- ment Anything in Images and Videos,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.654683Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:b033c05f7eac2c44814544cf8745bfa07506afe8cd2d32fd1c27a3a4ded66c1f","observation_id":"51a74605-5697-46bb-b4a5-2c15fd7d57f0","resolution":{"observed_at":"2026-08-06T12:37:57.654683Z","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":"2026-08-06T12:37:59.888061Z","title":"Segment anything in medical images,","venue":null,"work_id":"50740a11-3a5c-4071-86a4-7799f8d912b2","year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.826296Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:ba74385d9b0a4f022e043b5543aa1422efba8ede8ededc1762ae86d9b96d7fad","observation_id":"9128bce5-4bd2-4c52-bd16-f8dea789eac5","resolution":{"observed_at":"2026-08-06T12:37:59.918342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.808987Z","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,","venue":null,"work_id":"34248abc-108e-4c27-9830-b8da725331a8","year":2018},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:57.940717Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:26afaeeaed0b0bd9274b3b5b58190e74bc521e9861ecf89f5abf5fcec16fee64","observation_id":"9dc61416-9a7b-4894-976a-83a48e6e096f","resolution":{"observed_at":"2026-08-06T12:37:59.849792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03286","last_updated":"2024-08-17T12:56:51Z","snapshot_observed_at":"2026-08-12T23:07:42.855299Z","submitted_at":"2024-08-06T16:34:04Z","title":"Biomedical SAM 2: Segment Anything in Biomedical Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03286","snapshot_observed_at":"2026-08-06T12:37:58.081259Z","title":"Biomedical sam 2: Segment anything in biomedical images and videos,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.081259Z"},"links":{"cited_paper":"/paper/2408.03286","citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:88efe66cf8026a4d9bdccafb89fd0e264d28c12a5eca02f032b9a2686e35d9ab","observation_id":"a0f54efe-0e60-430f-8534-404aeb2ccbb1","resolution":{"observed_at":"2026-08-06T12:37:58.081259Z","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":"2026-08-06T12:37:59.729168Z","title":"Deeplab: Semantic image segmenta- tion with deep convolutional nets, atrous convolution, and fully connected crfs,","venue":null,"work_id":"265fc78e-4bad-4e9a-9aff-b5d816e8076b","year":2017},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.228266Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:4cd43b10d26d637787a545250c867d19886008ea5ffde4c5c184aabde1b2d6be","observation_id":"3c118467-08e8-4eb8-85dc-43ead5f78f8b","resolution":{"observed_at":"2026-08-06T12:37:59.769631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.672389Z","title":"Train Neural Net for Semantic Seg- mentation with PyTorch in 50 Lines of Code,","venue":null,"work_id":"c2e8858e-71fb-4f4c-939a-53ea3f26d0cb","year":2021},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.342320Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:b3d2a87a52d1d3783a525b0148b9ba28c51848e4a91e050bf3bc481199e08975","observation_id":"6b5177be-dfc1-4eb0-8867-5abffee8ec6b","resolution":{"observed_at":"2026-08-06T12:37:59.685161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.597795Z","title":"Video ob- ject segmentation using space-time memory networks,","venue":null,"work_id":"b56b052b-f9de-4d0a-8ff7-29c717903525","year":2019},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.385206Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:299588a1013f39b1f4ab7abfd52b63aedba251c091a12509dbdcbbecb5aeac07","observation_id":"1b78675a-3b2d-480d-ac21-57a771d906fc","resolution":{"observed_at":"2026-08-06T12:37:59.633983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.511836Z","title":"Segmentation of Carotid Plaques Based on Improved DeepLabV3,","venue":null,"work_id":"59776dec-26eb-46cb-bafe-59c062afb808","year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.455288Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:3a62d413b64acefdd1b2630104188541b4a904ebec16c73cbc2c86ec2a19a486","observation_id":"e1ec017b-6ed9-4aaf-957a-694c13732854","resolution":{"observed_at":"2026-08-06T12:37:59.551447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03678","last_updated":"2023-08-11T04:23:29Z","snapshot_observed_at":"2026-07-06T15:23:52.761222Z","submitted_at":"2023-05-05T16:48:45Z","title":"Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03678","snapshot_observed_at":"2026-08-06T12:37:58.480278Z","title":"Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Sur- vey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.480278Z"},"links":{"cited_paper":"/paper/2305.03678","citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:5627ae15fe970144a00c100503079b3c01fd0aa034564d6b6c8103a7dd707e49","observation_id":"28e38cec-142e-4f6a-9f40-4dfeebf825fd","resolution":{"observed_at":"2026-08-06T12:37:58.480278Z","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":"2026-08-06T12:37:59.428465Z","title":"Adapting segment anything models to medical imaging via fine-tuning without do- main pretraining,","venue":null,"work_id":"ea93df49-0533-4e9c-a037-ccfb584a5abd","year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.509194Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:acfa4be31f6126aa059c1ed2153cb15ed371aa13c80798efe15b729a0ba73847","observation_id":"d4ddca68-f705-4ef6-880e-a40ab8485138","resolution":{"observed_at":"2026-08-06T12:37:59.468141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.302188Z","title":"DeepLabV3 and Medical Imaging,","venue":null,"work_id":"babd5a83-05cb-4e9f-a57d-829a5690e576","year":2022},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.551010Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:bed8c011fc97653069bb73bf6af63e2455aba1e99193451fcc644828a27ac00f","observation_id":"2488df5b-aa0d-412f-843d-38df337d2079","resolution":{"observed_at":"2026-08-06T12:37:59.349410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.217258Z","title":"On the computational complexity of self-attention,","venue":null,"work_id":"c0b90e12-cff3-4bfd-a68c-943e23c2095e","year":2023},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.597513Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:f79e3537b2947cf5193b51794d47b9f3f3cc0d01452041f7d73e9f0fe0d3ca0c","observation_id":"dd9fdced-3008-45b3-bbd3-360bac8ac0c5","resolution":{"observed_at":"2026-08-06T12:37:59.255878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.114417Z","title":"Domain Adaptation Guide,","venue":null,"work_id":"8a2ef18f-3aa9-4aa9-aa77-8a3c7ae4264d","year":2023},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.629912Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:17a910de587aa7d71e70be43a98b21ba452a58e9993acf18f45cb559481aba30","observation_id":"5b828f1f-f49d-44d0-bddd-56527ee51a9e","resolution":{"observed_at":"2026-08-06T12:37:59.175534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2412.05012","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:37:58.832940Z","title":"SAMCL: Empowering SAM to Continually Learn from Dynamic Domains,","venue":null,"work_id":"52d243c2-d986-45be-ab3c-d753a23948a9","year":2024},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.643842Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:ac0f28647df1e956ce9a2522b674c3b9f3cb09a93848027106d12ed6c7b8a1c5","observation_id":"73f4fc37-714f-4452-914f-6a4d11be79af","resolution":{"observed_at":"2026-08-06T12:37:58.881238Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:59.033848Z","title":"Ad- vancing medical imaging informatics by deep learning- based domain adaptation,","venue":null,"work_id":"bc805017-b638-47e7-8b9c-8d550a5963fe","year":2020},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.681074Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:21d3c6120373287c0a4fc8bdd1ed7ff2511c1256784d095207691969b7ce664b","observation_id":"fa6bcdb1-b813-4c58-8807-761534652cad","resolution":{"observed_at":"2026-08-06T12:37:59.073670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T12:37:58.957843Z","title":"iPS-Semantic-Segmentation,","venue":null,"work_id":"afb7f6c4-fce3-48d1-b633-7b32dca1d900","year":2025},"citing_paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:37:58.713765Z"},"links":{"citing_paper":"/paper/2507.21608"},"observation_digest":"sha256:44a468160ba5744af4ef48c8518379e6bf1c15aae69eee1c0ca6b3d6cbc0a8af","observation_id":"fd476f0a-8201-4c05-9fab-c5eac8ea7861","resolution":{"observed_at":"2026-08-06T12:37:58.996772Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21608","last_updated":"2025-07-29T09:05:01Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T19:28:06.968604Z","submitted_at":"2025-07-29T09:05:01Z","title":"Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":18},"total_outbound_references":22},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2507.21608."}