{"as_of":"2026-08-12T08:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:63440afc1e7cabca7d66059b2f1952468fff36d4b6948a2051fa9595190d2c11","coverage":[{"denominator":148,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T11:43:08.732680Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2412.15058/citation-record","integrity":"/paper/2412.15058/integrity","json":"/paper/2412.15058/citation-record.json","paper":"/paper/2412.15058"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T11:43:08.370113Z","title":"Automatic segmentation of mandible in panoramic x-ray.Journal of Medical Imaging, 2(4):044003,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.370113Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:ac70d22983d8e6510160a2b605b051d52a8d33b7a8f2eea73653597659e01bed","observation_id":"3ebc6ef5-d500-4671-ae0e-1cf238245ad6","resolution":{"observed_at":"2026-08-11T11:43:08.370113Z","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":"2026-08-11T11:43:08.374545Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.374545Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:5ae78162d8bdf04b01752479f133429b9db1f54e4850d9da3f36ed76beff8740","observation_id":"d504675d-3500-4a11-83ab-7592b8583dfc","resolution":{"observed_at":"2026-08-11T11:43:08.374545Z","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":"2026-08-11T11:43:08.378304Z","title":"Dataset of breast ultrasound images","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.378304Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:2052d8669da2653eb50c249bb712c8daf5451def3c76cf36fefe3652082b684a","observation_id":"4d62631e-9997-4947-b106-59bd2e4c5219","resolution":{"observed_at":"2026-08-11T11:43:08.378304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.04584","last_updated":"2022-03-29T23:48:10Z","snapshot_observed_at":"2026-07-06T12:26:51.025323Z","submitted_at":"2022-01-12T17:21:28Z","title":"ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation","version":4},"cited_work":{"arxiv_id":"2201.04584","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.04584","snapshot_observed_at":"2026-08-11T11:43:09.291998Z","title":"ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation","venue":"eess.IV","work_id":"f8245805-d774-420c-8beb-9f2ee1ca83de","year":2022},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.382302Z"},"links":{"cited_paper":"/paper/2201.04584","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:991bc57535c3fc787777bd334e5380eff92c90e31381adf3d5e7397ba7e1f856","observation_id":"77385bb5-1f53-40bc-990b-b17106dcc68e","resolution":{"observed_at":"2026-08-11T11:43:09.296381Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.13696","last_updated":"2023-09-24T23:18:52Z","snapshot_observed_at":"2026-07-06T15:07:26.312584Z","submitted_at":"2023-03-23T22:20:56Z","title":"Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation","version":2},"cited_work":{"arxiv_id":"2303.13696","doi":null,"metadata_source":"pith","pith_arxiv_id":"2303.13696","snapshot_observed_at":"2026-08-11T11:43:09.277092Z","title":"Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation","venue":"eess.IV","work_id":"6d79c4aa-4a6c-4fe6-9c54-47cb4aabd676","year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.386658Z"},"links":{"cited_paper":"/paper/2303.13696","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:37611b11fa0e19ee1f44331f9ca66e46e6b12c53272e712948ac3dc7297d0310","observation_id":"2f64eb11-0699-43bc-8eb7-e666d3fd5aec","resolution":{"observed_at":"2026-08-11T11:43:09.281299Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.06450","last_updated":"2016-07-21T19:57:52Z","snapshot_observed_at":"2026-08-12T04:19:02.139595Z","submitted_at":"2016-07-21T19:57:52Z","title":"Layer Normalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.06450","snapshot_observed_at":"2026-08-11T11:43:08.390811Z","title":"Layer normalization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.390811Z"},"links":{"cited_paper":"/paper/1607.06450","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:512812731eb5ba4065e7595f10c1eb7150ad36057793557487d4ec02001d2078","observation_id":"e9b8ef8a-a6ca-49e2-ac4e-f3722d295d1c","resolution":{"observed_at":"2026-08-11T11:43:08.390811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.02314","last_updated":"2021-09-12T20:26:52Z","snapshot_observed_at":"2026-08-06T01:56:28.054412Z","submitted_at":"2021-07-05T23:12:06Z","title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.02314","snapshot_observed_at":"2026-08-11T11:43:08.394924Z","title":"The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.394924Z"},"links":{"cited_paper":"/paper/2107.02314","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:bd4c57513ecdacf7529dffe072722706e40e35e5366d4083f0acb54b6b880895","observation_id":"6912cdb5-1635-4953-96be-878b18c8a77f","resolution":{"observed_at":"2026-08-11T11:43:08.394924Z","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":"2026-08-11T11:43:08.398611Z","title":"Advancing the cancer genome atlas glioma mri collections with expert seg- mentation labels and radiomic features","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.398611Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:f41d43957fc9c98c0411103ad70baefb4d7782b6151cb1872f85113fc0c28e21","observation_id":"272e3001-b561-4515-927f-1d8b15ff7f3b","resolution":{"observed_at":"2026-08-11T11:43:08.398611Z","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":"2026-08-11T11:43:08.402269Z","title":"Deep placental vessel segmentation for fetoscopic mosaicking","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.402269Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:85054b1c252919749d8e4a523e7d93b67ff4f8e797ae3996fb369000991b7d51","observation_id":"181d59d1-53ba-471b-afc7-6876293bdafb","resolution":{"observed_at":"2026-08-11T11:43:08.402269Z","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":"2026-08-11T11:43:08.405786Z","title":"Large-scale interactive object segmentation with human an- notators","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.405786Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:1cc49b70ff8bed54e77567e1cad7e48d28a0f67cd4b01af6f564ccccfceb9a09","observation_id":"e48d2b5e-90f3-4562-a65a-a2352c53a295","resolution":{"observed_at":"2026-08-11T11:43:08.405786Z","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":"2026-08-11T11:43:08.408847Z","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":null,"year":2018},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.408847Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:6f96c8417689fea6f2f794da5d6408e204f36a36b5d84bbebec41ea8ba9b21e8","observation_id":"c7b15f8d-786d-457a-bcea-c01c51773765","resolution":{"observed_at":"2026-08-11T11:43:08.408847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1901.04056","last_updated":"2022-11-25T09:24:35Z","snapshot_observed_at":"2026-07-06T07:26:26.981747Z","submitted_at":"2019-01-13T20:38:16Z","title":"The Liver Tumor Segmentation Benchmark (LiTS)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.04056","snapshot_observed_at":"2026-08-11T11:43:08.412417Z","title":"The liver tumor segmentation benchmark (lits)","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.412417Z"},"links":{"cited_paper":"/paper/1901.04056","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:96b51f46b6643b184cdc8e6743f22a2e3f941027885982a948ca07f1b1929243","observation_id":"cb1c83e3-2ab1-47ff-965a-a1f4e6d05511","resolution":{"observed_at":"2026-08-11T11:43:08.412417Z","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":"2026-08-11T11:43:08.416654Z","title":"Synthseg: Segmenta- tion of brain mri scans of any contrast and resolution with- out retraining","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.416654Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:293c7a46107337bc0259dfbc71e6634d4778d9a2f959092e9f8a94a2eaf2abc6","observation_id":"fd41ea69-9c8a-4a62-bda9-c83ac5d3e446","resolution":{"observed_at":"2026-08-11T11:43:08.416654Z","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":"2026-08-11T11:43:08.420460Z","title":"Nci-isbi 2013 challenge: automated segmentation of prostate structures","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.420460Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:a57c3b30afd330d432d0c56cb858915972d7f7a8207e07a6346fdc39a8e7a756","observation_id":"0500ac5c-5750-4e73-91c1-a3255e9aed22","resolution":{"observed_at":"2026-08-11T11:43:08.420460Z","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":"2026-08-11T11:43:08.424169Z","title":"Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learn- ing algorithm","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.424169Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:2095c1d665a8239c28a1b00f1905da5d4fe8d2f53cf67bbaca0d54e966d862ae","observation_id":"743b79f7-b858-485b-9eaa-e26a7cfd307c","resolution":{"observed_at":"2026-08-11T11:43:08.424169Z","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":"2026-08-11T11:43:08.427935Z","title":"Sabuncu, John Guttag, and Adrian V","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.427935Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:4f4f7cc35fced659972642946e1d3922e35f9e8dbc3ba7526b13bf9068eab20a","observation_id":"44efb549-4b29-4dbc-bf1a-3f1a11f445e3","resolution":{"observed_at":"2026-08-11T11:43:08.427935Z","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":"2026-08-11T11:43:08.435759Z","title":"Caicedo, Allen Goodman, Kyle W","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.435759Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:cf3cc1e669b75dc8b5dc43c8f0fdeb4b91fa1667a1f1e38fff530020bc6b958e","observation_id":"4ea8bbe3-2939-4877-90a2-09289c0c529b","resolution":{"observed_at":"2026-08-11T11:43:08.435759Z","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":"2026-08-11T11:43:08.439442Z","title":"An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy.PLoS biology, 8(10):e1000502, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.439442Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:02599764532d0b322593e0918d2d19b7b8056cd1d77beb92bad86723d938ea19","observation_id":"addef999-dfdc-4d1f-af40-51d1b14b1eb2","resolution":{"observed_at":"2026-08-11T11:43:08.439442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.16184","last_updated":"2023-08-30T17:59:02Z","snapshot_observed_at":"2026-07-06T16:12:26.218372Z","submitted_at":"2023-08-30T17:59:02Z","title":"SAM-Med2D","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.16184","snapshot_observed_at":"2026-08-11T11:43:08.443152Z","title":"SAM-Med2D, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.443152Z"},"links":{"cited_paper":"/paper/2308.16184","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:c90ca92bbecfba2dd2f159e2d66059559740a39fb60178cfbb3394d49fd782c3","observation_id":"7347a76e-917e-48c6-9a73-472bdaaf7115","resolution":{"observed_at":"2026-08-11T11:43:08.443152Z","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":"2026-08-11T11:43:08.447233Z","title":"Interactive medical image segmentation: A bench- mark dataset and baseline","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.447233Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:8cadc573edaf8a3a41a431c5725daee863ddfae4d81ee01938a472a599658c05","observation_id":"86508ace-ce21-4cbb-9554-fa49b2a21c33","resolution":{"observed_at":"2026-08-11T11:43:08.447233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05006","last_updated":"2018-01-08T16:37:20Z","snapshot_observed_at":"2026-07-06T06:04:09.462436Z","submitted_at":"2017-10-13T17:08:53Z","title":"Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05006","snapshot_observed_at":"2026-08-11T11:43:08.450694Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.450694Z"},"links":{"cited_paper":"/paper/1710.05006","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:b730f9f61c7e455742a6b4ad36deb6c8392c2ff843a50c44aec1ffe507050778","observation_id":"02c3aff1-9d7c-40c5-ad7f-db47206a0bd0","resolution":{"observed_at":"2026-08-11T11:43:08.450694Z","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":"2026-08-11T11:43:08.454563Z","title":"Neuralizer: General neuroimage analysis without re-training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.454563Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:0a67a9aff0f4e3a4535dc0ad5360dc92525f5881567c529cf8dfbe840c4c2b97","observation_id":"ee07ff2c-34fb-455e-8886-e0f5f0e7a6af","resolution":{"observed_at":"2026-08-11T11:43:08.454563Z","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":"2026-08-11T11:43:08.458020Z","title":"Anatomical priors in convolutional networks for unsuper- vised biomedical segmentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.458020Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:368f6c12c8add727311a635527c5e2727217f9cb40c454f4dbd609ea01b489af","observation_id":"3a4dc585-f156-4f1e-954e-bf72d0f4e112","resolution":{"observed_at":"2026-08-11T11:43:08.458020Z","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":"2026-08-11T11:43:08.461605Z","title":"Anatomical priors in convolutional networks for unsuper- vised biomedical segmentation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.461605Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:309821ff91e0159f11f1fdf6ecf35cd197496e859bd0d93af8aa2d7053bb06f7","observation_id":"b25d53db-206f-44be-9c38-b6374dd6fa2c","resolution":{"observed_at":"2026-08-11T11:43:08.461605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02075","last_updated":"2025-08-21T14:39:31Z","snapshot_observed_at":"2026-08-10T01:50:04.618631Z","submitted_at":"2024-07-02T09:08:06Z","title":"Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02075","snapshot_observed_at":"2026-08-11T11:43:08.465409Z","title":"Label anything: Multi-class few- shot semantic segmentation with visual prompts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.465409Z"},"links":{"cited_paper":"/paper/2407.02075","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:7c1557aa3623e21aa53a298cad4b599f662f54d3eb870642188b37dfeadd715d","observation_id":"dddf92bf-8bf5-4c48-8c52-6e349354b1e1","resolution":{"observed_at":"2026-08-11T11:43:08.465409Z","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":"2026-08-11T11:43:08.469359Z","title":"Teleophta: Machine learning and image process- ing methods for teleophthalmology","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.469359Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:d4ca025a0f83722e48aef8fd1eec3e026bcf117bd5ba7c96ca29e8015483a1de","observation_id":"738cdb27-7bb9-4b23-aac5-976a972393cb","resolution":{"observed_at":"2026-08-11T11:43:08.469359Z","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":"2026-08-11T11:43:08.473002Z","title":"Early detection of myocardial infarction in low- quality echocardiography","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.473002Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:0caaedad626b46d011ddb48dbbd1049e82506bc22ba6d8d6a625f37491486113","observation_id":"de58b698-1d7f-4014-af33-ff007ba6779c","resolution":{"observed_at":"2026-08-11T11:43:08.473002Z","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":"2026-08-11T11:43:08.476462Z","title":"Monai label: A framework for ai-assisted interactive label- ing of 3d medical images","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.476462Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:cfe7223d2d57808830fafb26bf4b036cc16e4005acc5239d4148b09f53565a20","observation_id":"3d85fa97-89d7-478a-bbd2-a21b81e11c58","resolution":{"observed_at":"2026-08-11T11:43:08.476462Z","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":"2026-08-11T11:43:08.479404Z","title":"Measures of the amount of ecologic association between species","venue":null,"work_id":null,"year":1945},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.479404Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:fdc7509ccd0bdf09f909a79e395a347494ac3724e1a32fffcd5ab4f19da46099","observation_id":"00ef2ed0-9400-4be2-bf6f-3a5a49be0dc3","resolution":{"observed_at":"2026-08-11T11:43:08.479404Z","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":"2026-08-11T11:43:08.482564Z","title":"Efficient graph-based image segmentation","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.482564Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:cb0899ee529b9ec1a50e2da668374731d67ef71047f683755c275d42ca45d4ee","observation_id":"7b7cf13b-07f0-4855-97c6-ed9017185778","resolution":{"observed_at":"2026-08-11T11:43:08.482564Z","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":"2026-08-11T11:43:08.485675Z","title":"Freesurfer","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.485675Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:1906e4eea127d47027e3ee68d0316c844891b600ad5b1d0546bda6997823c860","observation_id":"8e1d478b-24b1-415e-983f-adf4478e89aa","resolution":{"observed_at":"2026-08-11T11:43:08.485675Z","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":"2026-08-11T11:43:08.488863Z","title":"Pan- nuke dataset extension, insights and baselines","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.488863Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:4970daa1959de8822d4478223c98d60c14e9ca7b6d6ae7e43d4373241e290ef5","observation_id":"23fd4a5a-f348-4424-8e06-193e0215d255","resolution":{"observed_at":"2026-08-11T11:43:08.488863Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T11:43:08.492823Z","title":"Boosting your context by dual similarity checkup for in-context learning medical im- age segmentation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.492823Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:bdd60c8cbf7d4acf6b6b9e0e319083e1312cad897c177e47af6c49fed537c553","observation_id":"088270c0-a041-47b6-abeb-b0f5a1dd522e","resolution":{"observed_at":"2026-08-11T11:43:08.492823Z","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":"2026-08-11T11:43:08.496407Z","title":"Segmented anisotropic ssTEM dataset of neural tissue","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.496407Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:a9c07348cea7f7af4dce2c50e5d9ee0da5c99780d77eb1e97efac040f6cd1518","observation_id":"dd131c6b-d5e5-4df2-9cd7-bd28e628b15b","resolution":{"observed_at":"2026-08-11T11:43:08.496407Z","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":"2026-08-11T11:43:08.499934Z","title":"The mcic collection: a shared repository of multi- modal, multi-site brain image data from a clinical inves- tigation of schizophrenia","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.499934Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:474f3fd0810a2848e930421d0d680ef9851dfad78394fc4bd7fd60c3caab816c","observation_id":"63288b1d-b45a-4064-9ca6-22c346468f6b","resolution":{"observed_at":"2026-08-11T11:43:08.499934Z","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":"2026-08-11T11:43:08.503672Z","title":"Synthetic data in generalizable, learning-based neuroimaging","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.503672Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:1b6528662cb39100d5e98d76074d4080a2789d31158c96a2e5261c9728e65c7b","observation_id":"fbfc86be-8441-4e93-9777-184c0d490eab","resolution":{"observed_at":"2026-08-11T11:43:08.503672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12834","last_updated":"2025-08-13T11:28:51Z","snapshot_observed_at":"2026-08-11T01:41:49.267940Z","submitted_at":"2024-03-19T15:41:16Z","title":"Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12834","snapshot_observed_at":"2026-08-11T11:43:08.507394Z","title":"Embar- rassingly simple scribble supervision for 3d medical seg- mentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.507394Z"},"links":{"cited_paper":"/paper/2403.12834","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:90294d00b9bccefe1bb9c370dc290feb56e57c71ef73a13d8786644a672e68f8","observation_id":"0c396bcd-2398-4b74-934d-a0d12c3ce061","resolution":{"observed_at":"2026-08-11T11:43:08.507394Z","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":"2026-08-11T11:43:08.511232Z","title":"Automatic segmentation of brain mris of 2-year-olds into 83 regions of interest","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.511232Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:c934fa543b729e8387e0f2f0b57bbe896f47f355d60e3156fe0e448c10f6e395","observation_id":"11a687d4-13da-45f8-b681-a48bb129b3d5","resolution":{"observed_at":"2026-08-11T11:43:08.511232Z","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":"2026-08-11T11:43:08.514865Z","title":"Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.514865Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:d393e6b56563b62abb3881cc5c4ccc720c8bed895c1b1c1ceda0a2cefc99638d","observation_id":"ad234f6f-3d8c-4c50-ae0c-90ec920d5119","resolution":{"observed_at":"2026-08-11T11:43:08.514865Z","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":"2026-08-11T11:43:08.518520Z","title":"Hover-net: Simultaneous segmentation and classi- fication of nuclei in multi-tissue histology images","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.518520Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:cbcc8d226e7e26022d54d2e78c8a41654ecbdc348a89fbd91adbb7610afb28d2","observation_id":"269d5160-62d3-441e-bc98-1b0b53042ba9","resolution":{"observed_at":"2026-08-11T11:43:08.518520Z","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":"2026-08-11T11:43:08.522178Z","title":"Deep learning enables automatic detection and segmentation of brain metastases on multisequence mri","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.522178Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:c6a2ac068b365714945e89f27ef0197e2c931379c3c044e671b30595746da62c","observation_id":"1baeb2bd-9a23-4d13-8687-44df9369dfe1","resolution":{"observed_at":"2026-08-11T11:43:08.522178Z","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":"2026-08-11T11:43:08.525712Z","title":"X-ray images of the hip joints","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.525712Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:d791fa4ddf35d98df29a856465a7f1948f093b79163863eb42cd538551de1e79","observation_id":"5918130d-0b7b-4c31-a8a8-0a677ac2eb52","resolution":{"observed_at":"2026-08-11T11:43:08.525712Z","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":"2026-08-11T11:43:08.529292Z","title":"The state of the 10 art in kidney and kidney tumor segmentation in contrast- enhanced ct imaging: Results of the kits19 challenge","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.529292Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:84346a51fc560191cc21ff9c215e007b3089ac73f0f3b4084fb16323a475aaed","observation_id":"6daec8e6-fd3c-43a9-9b86-fceec94cb3fe","resolution":{"observed_at":"2026-08-11T11:43:08.529292Z","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":"2026-08-11T11:43:08.533025Z","title":"Isles 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.533025Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:be20e66b194051c2fca394b54fb1c0a8819345fe4daa9dc5e5dd799ca0d5a9e9","observation_id":"9f6ef529-c0c1-4cbd-8df9-15da22621e6f","resolution":{"observed_at":"2026-08-11T11:43:08.533025Z","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":"2026-08-11T11:43:08.536512Z","title":"Greve, Bruce Fischl, John Guttag, and Adrian V","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.536512Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:0455a1d158803849b9415b21455a109bb9cf53c21e7290a38ca17fe0950ee756","observation_id":"85e063b1-ade7-4a1a-9d0e-a3ab20e3fabd","resolution":{"observed_at":"2026-08-11T11:43:08.536512Z","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":"2026-08-11T11:43:08.540083Z","title":"Locating blood vessels in retinal images by piece- wise threshold probing of a matched filter response","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.540083Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:03f83ade316af4b983844b002cd546911ad61286049e3118d22db45c784560e8","observation_id":"10e1d824-59e7-4e7e-8f10-13bf08277be9","resolution":{"observed_at":"2026-08-11T11:43:08.540083Z","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":"2026-08-11T11:43:08.543661Z","title":"Icl-sam: Synergizing in-context learn- ing model and sam in medical image segmentation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.543661Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:bf9b6e7dc81dd3cc116a97c62788c806d36edf4368fb5e0e99b5840a30698480","observation_id":"43e22e98-ae00-42a2-a298-8139da54ad59","resolution":{"observed_at":"2026-08-11T11:43:08.543661Z","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":"2026-08-11T11:43:08.547080Z","title":"How to efficiently adapt large segmentation model(sam) to medical images,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.547080Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:f3db926370c8476820b2ca8bac3e65e84a3f2108078e5a5afbff2bfd1d77bcad","observation_id":"32593623-e883-429c-9360-c1d7ecff2419","resolution":{"observed_at":"2026-08-11T11:43:08.547080Z","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":"2026-08-11T11:43:08.550358Z","title":"Interformer: Real-time interactive image segmentation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.550358Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:c1c0ee27449e0270a685b65cc9a80fceb799117447a19b88b4765a9155a86a07","observation_id":"70ac20ad-998f-454f-9f50-ecbe31c444c4","resolution":{"observed_at":"2026-08-11T11:43:08.550358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.15901","last_updated":"2024-08-17T23:49:15Z","snapshot_observed_at":"2026-08-04T15:50:32.496344Z","submitted_at":"2024-03-23T18:04:58Z","title":"MatchSeg: Towards Better Segmentation via Reference Image Matching","version":3},"cited_work":{"arxiv_id":"2403.15901","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.15901","snapshot_observed_at":"2026-08-11T11:43:09.082271Z","title":"MatchSeg: Towards Better Segmentation via Reference Image Matching","venue":"cs.AI","work_id":"ef891df1-0615-4e9e-a57e-d6e48716a563","year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.553399Z"},"links":{"cited_paper":"/paper/2403.15901","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:f24b6e2b50f63f05f7d979415d111351810ceed39826c6b7c21c32c947183132","observation_id":"cc345657-9ac7-4960-884e-56f6d700e222","resolution":{"observed_at":"2026-08-11T11:43:09.196435Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:08.557201Z","title":"Klanderman, and William J Rucklidge","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.557201Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:ea826d9dce1b53d42b28b1a018997aef34f865780acd1a2ba8988a51625d6c0d","observation_id":"a1b71604-7e7d-4a89-b2ae-e64a6feff373","resolution":{"observed_at":"2026-08-11T11:43:08.557201Z","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":"2026-08-11T11:43:08.560799Z","title":"Teeth segmentation dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.560799Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:da147312c0b3cf9fbb87ced8c22831593c3171b34dfb77fd2466c7e93e7ba061","observation_id":"0c501382-2a35-4e50-af28-a46cd123ccb5","resolution":{"observed_at":"2026-08-11T11:43:08.560799Z","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":"2026-08-11T11:43:08.564412Z","title":"Jaeger, Simon A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.564412Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:beb035c68010dc6125c39cb344989868b1c340deec24223d9546242b07aeaf72","observation_id":"1776a325-0705-4623-9cfb-e91bfc558a05","resolution":{"observed_at":"2026-08-11T11:43:08.564412Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.08023","last_updated":"2022-09-02T01:32:31Z","snapshot_observed_at":"2026-08-06T03:20:59.184318Z","submitted_at":"2022-06-16T09:27:56Z","title":"AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.08023","snapshot_observed_at":"2026-08-11T11:43:08.567815Z","title":"Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation.arXiv preprint arXiv:2206.08023, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.567815Z"},"links":{"cited_paper":"/paper/2206.08023","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:ffa7a50a67dacbb16a98a3509d14d53199e7e5269a251fd3b49c263866b75f95","observation_id":"7c8e0fae-4034-4fca-87bf-9d8c3fa5c706","resolution":{"observed_at":"2026-08-11T11:43:08.567815Z","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":"2026-08-11T11:43:08.571262Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.571262Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:b71ddc517e9e092ba7e249002e6219a8de6d698c63cd1c33bae22db8f682a3bf","observation_id":"dfa164fd-ad69-4503-ba2a-bdecdd9d8c87","resolution":{"observed_at":"2026-08-11T11:43:08.571262Z","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":"2026-08-11T11:43:08.574772Z","title":"Alper Selver, O ˘guz Dicle, Mustafa Barıs ¸, and N","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.574772Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:8ede5f012536fd77fe971ab7437c3729a7bd28bde56f1f42f19d280100fc23f2","observation_id":"14873762-f10b-414c-899e-f27a878d4851","resolution":{"observed_at":"2026-08-11T11:43:08.574772Z","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":"2026-08-11T11:43:08.578422Z","title":"Emre Kavur, N","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.578422Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:b16a30a2390d69c52a8cfea3307efe7aa5c0558144af22992030c4b6688ca03e","observation_id":"162ed2d9-bf61-446d-a9b3-8e87a5aae19f","resolution":{"observed_at":"2026-08-11T11:43:08.578422Z","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":"2026-08-11T11:43:08.581851Z","title":"Evaluation and improvement of segment anything model for interactive histopathology image segmentation,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.581851Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:b70cc8ddf983dd22958202b89f490b480bc3a91c2297b4580eee15b0281323e8","observation_id":"a6db1bfa-833b-42db-9052-7bb76f1007b1","resolution":{"observed_at":"2026-08-11T11:43:08.581851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-11T11:43:08.585412Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.585412Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:f712433467861c3af1d81c9975e94a99fd4ce2c1815cd67139cb2861a7b9a3af","observation_id":"c2fa8dd4-26fb-4d35-9e30-8c83a02d7ca0","resolution":{"observed_at":"2026-08-11T11:43:08.585412Z","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":"2026-08-11T11:43:08.589152Z","title":"Left ventricular wall motion estimation by ac- tive polynomials for acute myocardial infarction detection","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.589152Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:8047826ea0f200fcd339ab719a5780d9c3c9a6b043d3be2f1fe72ac3cd0540ae","observation_id":"f6d72411-f2cc-461c-b319-7decaabce7c6","resolution":{"observed_at":"2026-08-11T11:43:08.589152Z","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":"2026-08-11T11:43:08.593468Z","title":"Berg, Wan-Yen Lo, Piotr Doll´ar, and Ross Girshick","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.593468Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:44a7f74fbaff81fce090cabbf839e9a105e44bfbaea86265c101f5075f3d5bce","observation_id":"a903d25f-c5b3-47c7-990a-3b1db921bed6","resolution":{"observed_at":"2026-08-11T11:43:08.593468Z","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":"2026-08-11T11:43:08.596928Z","title":"Continuous adaptation for interac- tive object segmentation by learning from corrections","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.596928Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:fb7442c5b26b82a5f67102c80622b1027f56b2db6589892629dc8738d83334fc","observation_id":"84f1d8ab-3dfa-4ce9-848d-55c558d569c3","resolution":{"observed_at":"2026-08-11T11:43:08.596928Z","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":"2026-08-11T11:43:08.599767Z","title":"Tracked 3d ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.599767Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:f8900715a0da1c4a02128f98f8a8168d82dac605e3a2a62f5b947de9cfc1dbe1","observation_id":"3244dc39-f5df-4b23-9cea-53ba649e9565","resolution":{"observed_at":"2026-08-11T11:43:08.599767Z","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":"2026-08-11T11:43:08.602638Z","title":"Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.602638Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:9d92aac06babfb75a46f822c867a2766e90a5db9928b7509669f6cd8d864895a","observation_id":"7e989c14-16dd-49fe-8069-66ef74ba0d70","resolution":{"observed_at":"2026-08-11T11:43:08.602638Z","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":"2026-08-11T11:43:08.605730Z","title":"A dynamic 4d probabilistic atlas of 11 the developing brain.NeuroImage, 54(4):2750–2763, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.605730Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:46ccc22518a0e2115890c48bb88019f7e3717b869f67c76b22116be10571443f","observation_id":"15300895-c53f-4018-9239-298b75fb0bb0","resolution":{"observed_at":"2026-08-11T11:43:08.605730Z","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":"2026-08-11T11:43:08.608957Z","title":"Segthor: segmentation of thoracic organs at risk in ct images","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.608957Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:882340d75bf5af66886a997b366a43ee8badaad09f7ad6f662720b18a4ed8bd5","observation_id":"acf21a89-0aac-46a5-bbfb-ffecf0a89595","resolution":{"observed_at":"2026-08-11T11:43:08.608957Z","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":"2026-08-11T11:43:08.613210Z","title":"Discobox: Weakly supervised instance segmentation and semantic correspondence from box su- pervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.613210Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:e384b84f67c2cbbb3c7250593419d3efe64393c431bca5d725e38a9dbfd11168","observation_id":"49bac93f-25d3-433c-a674-56ed9e1495d9","resolution":{"observed_at":"2026-08-11T11:43:08.613210Z","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":"2026-08-11T11:43:08.616599Z","title":"Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.616599Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:3ac5c8870721bdf485e653451751cf164cbedd2ce8404bb8a529f885fa98a1d4","observation_id":"edd649f0-bf93-4a9d-b1fc-4a0c6188aeae","resolution":{"observed_at":"2026-08-11T11:43:08.616599Z","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":"2026-08-11T11:43:08.619903Z","title":"Deep learning for segmentation using an open large-scale dataset in 2d echocardiography","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.619903Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:c238c880a581a6b17a60ce77b498d77b14d8d0ead82df9dd780cc0e46ab52aa8","observation_id":"649fa864-6a11-48ae-8e64-f32e090e4b9c","resolution":{"observed_at":"2026-08-11T11:43:08.619903Z","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":"2026-08-11T11:43:08.623842Z","title":"Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric mri: a review","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.623842Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:1180ea071d7922fac4c2e1dddedea3f94d89ebbcfacb1d9f80337bc8502bfd13","observation_id":"899dd8f3-4b3a-43d2-9119-9d43c630e2bc","resolution":{"observed_at":"2026-08-11T11:43:08.623842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.07261","last_updated":"2022-12-25T23:42:44Z","snapshot_observed_at":"2026-07-31T18:48:43.871831Z","submitted_at":"2020-12-14T05:20:29Z","title":"OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study","version":3},"cited_work":{"arxiv_id":"2012.07261","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.07261","snapshot_observed_at":"2026-08-11T11:43:09.045366Z","title":"OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study","venue":"eess.IV","work_id":"4ab3a9f1-35ad-488d-84e5-337a5bd6dfc4","year":2020},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.627709Z"},"links":{"cited_paper":"/paper/2012.07261","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:d45b65505ccb1a22e3d003286a34aeccf235e9c0b3786d0ac55c3c8d73248108","observation_id":"6ca5610b-55f1-4c41-ab38-d9cbac8507e1","resolution":{"observed_at":"2026-08-11T11:43:09.051369Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:08.631460Z","title":"Scribblevc: Scribble-supervised medical im- age segmentation with vision-class embedding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.631460Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:227a55ec6aff0261ce60768b3b5fb287a6a737acfb309c87551e7f577820a19c","observation_id":"967d036e-5620-4483-8d91-f1ab56168e85","resolution":{"observed_at":"2026-08-11T11:43:08.631460Z","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":"2026-08-11T11:43:08.635093Z","title":"Scribblesup: Scribble-supervised convolutional networks for semantic segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.635093Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:ce441326aa4b5e5d45da71fb3c38f3590c58ec8edb73bc394f2877db662582d9","observation_id":"ba108cd6-994e-4364-833e-f2fa120293d9","resolution":{"observed_at":"2026-08-11T11:43:08.635093Z","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":"2026-08-11T11:43:08.639010Z","title":"Focal loss for dense object detection","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.639010Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:a83d968ff197ce72de54e484f9d1013edaf4f467e30d720eb9b8dd943123d62c","observation_id":"dd219d38-1427-4193-a8a1-39aec476fe4e","resolution":{"observed_at":"2026-08-11T11:43:08.639010Z","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":"2026-08-11T11:43:08.642235Z","title":"Samus: Adapting segment any- thing model for clinically-friendly and generalizable ultra- sound image segmentation, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.642235Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:a43494b5d094dea04bb06a72e3eec398df188d3013bf35d843c1a3153b4ad69e","observation_id":"ae7ed6ea-3e51-4e7e-960e-4ac75f00d8e9","resolution":{"observed_at":"2026-08-11T11:43:08.642235Z","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":"2026-08-11T11:43:08.645733Z","title":"Evaluation of prostate segmentation algorithms for mri: the promise12 challenge","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.645733Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:4b6946dfc478daa831d2558927178f46284723aee7539a4a09962a3e5d269b82","observation_id":"802767d5-aa6d-48b8-8fd3-415736b2cd68","resolution":{"observed_at":"2026-08-11T11:43:08.645733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09732","last_updated":"2023-07-19T02:49:44Z","snapshot_observed_at":"2026-07-06T15:55:41.294452Z","submitted_at":"2023-07-19T02:49:44Z","title":"ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09732","snapshot_observed_at":"2026-08-11T11:43:08.649221Z","title":"Clickseg: 3d instance segmentation with click-level weak annotations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.649221Z"},"links":{"cited_paper":"/paper/2307.09732","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:15a53948871d01c67ceab576df0c2710b5ad0e74bd139a6a6b2fd8fe9f1a9838","observation_id":"d0a19fce-2622-4aac-bd97-71ded5dee81e","resolution":{"observed_at":"2026-08-11T11:43:08.649221Z","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":"2026-08-11T11:43:08.652523Z","title":"Simpleclick: Interactive image segmentation with simple vision transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.652523Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:2ec75aafc9d23259ee73d4fd87498a6c62ab7467e1015b3d4119670ed4f9cd4b","observation_id":"fff67beb-a2af-4b4e-bd60-80178ac7bf86","resolution":{"observed_at":"2026-08-11T11:43:08.652523Z","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":"2026-08-11T11:43:08.655607Z","title":"Rethinking interactive image segmentation with low latency high quality and diverse prompts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.655607Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:a1d925ca5a7506e22df8b573646d0dda572122969426f54d1970a493d390de31","observation_id":"c4160072-90a5-472d-b458-5de022484358","resolution":{"observed_at":"2026-08-11T11:43:08.655607Z","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":"2026-08-11T11:43:08.658667Z","title":"The effects of interactive la- tency on exploratory visual analysis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.658667Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:af8d91025e5745a3849a49c6fa519a1e22e22418ce5367055ec7fd8dcbee2c60","observation_id":"4ade8944-4f2d-4009-9b1b-075231598f64","resolution":{"observed_at":"2026-08-11T11:43:08.658667Z","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":"2026-08-11T11:43:08.661863Z","title":"One thing one click: A self-training approach for weakly supervised 3d semantic segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.661863Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:fcbb6d7690fd98080f815d29dfcb1c48e833cd2548f82d1b8b944fcca2a052a8","observation_id":"4f1d2daa-0a90-4e59-a416-9049c40340c1","resolution":{"observed_at":"2026-08-11T11:43:08.661863Z","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":"2026-08-11T11:43:09.887597Z","title":"Annotated high-throughput microscopy image sets for validation","venue":null,"work_id":"5fc6bfb2-eedd-43c9-b1a1-1e99e31f5856","year":2012},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.665701Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:1cd8428ac3c9fa5393128cf7212267bd5785b8c4c063eee485b751d0dd715b91","observation_id":"9fae4ce4-d6cb-4f62-8ba0-b7c050b81e39","resolution":{"observed_at":"2026-08-11T11:43:09.891573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.876509Z","title":"A vertebral segmentation dataset with fracture grading","venue":null,"work_id":"9c780f2c-4ee1-43ba-af5a-7958ec99ddd8","year":null},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.669427Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:c54fda5274832abe64e04d78fd1d28492d8437e4758be739d02c7d7b42ee05b0","observation_id":"879b783e-faf6-471f-9993-aa6c68fa0e0f","resolution":{"observed_at":"2026-08-11T11:43:09.880593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.02403","last_updated":"2023-02-13T03:24:50Z","snapshot_observed_at":"2026-08-12T06:24:55.226084Z","submitted_at":"2021-11-03T02:26:14Z","title":"WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.02403","snapshot_observed_at":"2026-08-11T11:43:08.672917Z","title":"Word: Revisiting organs segmentation in the whole abdom- inal region","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.672917Z"},"links":{"cited_paper":"/paper/2111.02403","citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:957981faf8c51310434a5ade8cf9846d461192e744f9d23d9b14c9e464a2e383","observation_id":"e3f361fd-b401-4f69-a45e-b6091627146b","resolution":{"observed_at":"2026-08-11T11:43:08.672917Z","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":"2026-08-11T11:43:09.865927Z","title":"Mideepseg: Minimally interactive segmentation of unseen objects from medical images using deep learning","venue":null,"work_id":"92cd39bb-b133-4b60-b14c-8b94d4265a90","year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.677909Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:bada1de19cc127d4350778ad67e40e659479202bcf25bc4508b7ee92573ea56e","observation_id":"1cff3001-7111-468b-ab1e-043b5f42e053","resolution":{"observed_at":"2026-08-11T11:43:09.869593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.855869Z","title":"Scribble- supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision","venue":null,"work_id":"795d9aa0-a004-4664-8900-a89918a6cfe9","year":2022},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.681407Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:999749333e6ba08d3361da5cdf3eb4c6e179b796956b8d079ec8da70f7a8b111","observation_id":"43533e60-f29c-4eb3-9d3e-9f7a1c915131","resolution":{"observed_at":"2026-08-11T11:43:09.859674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.846751Z","title":"Fast and low-gpu-memory abdomen ct organ seg- mentation: the flare challenge","venue":null,"work_id":"928e2c81-ce56-4664-ac58-f10f5847f43d","year":2022},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.684950Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:9b2bb4fde3aec6f34eb3153f2e1182128141a9eb55292c74349ef318e8e317ab","observation_id":"e6342922-42da-4460-bd95-04cd6ed09c7c","resolution":{"observed_at":"2026-08-11T11:43:09.849805Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.837586Z","title":"Segment anything in medical images","venue":null,"work_id":"c8a09df2-14bd-4a0d-8c02-5e7e6fc96654","year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.688589Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:6807c63ea31d905d93248eea731afc95efd66aefdb3ee8a97a9d4f8eafa3969f","observation_id":"965df361-9f31-4076-9f55-ef46e395f368","resolution":{"observed_at":"2026-08-11T11:43:09.840640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.826728Z","title":"Rose: a retinal oct-angiography vessel segmentation dataset and new model","venue":null,"work_id":"a3e284f3-9692-4c53-bf15-8c8661c2781a","year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.693460Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:6d57cd1af6547cc0e96c029d72d448d96bbfb6ab9f1bad8fdf77981bf39381c8","observation_id":"27fc4bf7-664d-4bed-87ea-d3b79571e94a","resolution":{"observed_at":"2026-08-11T11:43:09.830425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.815874Z","title":"Macdonald, Zhe Zhu, Brandon Konkel, Maciej Mazurowski, Walter Wiggins, and Mustafa Bashir","venue":null,"work_id":"34099ddc-589a-417d-8043-a5ffb9e28aca","year":2023},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.697556Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:9f69c07085e42478bf689324bd5c2c883d33034c0d626186003265850bf43dcc","observation_id":"72331882-ce6d-43ac-a8f4-6c2eb1da9225","resolution":{"observed_at":"2026-08-11T11:43:09.819683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.804513Z","title":"Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults","venue":null,"work_id":"0f5e6900-90e5-4919-b869-f6f2c89b04c0","year":2007},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.701300Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:360f6e1564dc4408294b66a0012942f01d3979985adda5168b7ab3a1937506ea","observation_id":"b1f6fc1b-f508-4c7d-9339-f826866d6188","resolution":{"observed_at":"2026-08-11T11:43:09.808794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.793220Z","title":"The parkinson progression marker initiative (ppmi)","venue":null,"work_id":"1e7e454a-2231-4727-af9e-3dc40c4d49ef","year":2011},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.704577Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:1a53b596e325a72bd87b99d756c5f2f9dd7b074cea382d6c418f40296e9a2511","observation_id":"b92d7dcb-5da3-4806-bc68-1ff9f7048846","resolution":{"observed_at":"2026-08-11T11:43:09.797174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.782760Z","title":"Meiburger","venue":null,"work_id":"4c5e4b75-5d8e-400a-b08e-3322b6d7eb80","year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.707563Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:ab174053e45e81c4711b1bf7d0126460dc3d9b6a04fbbcfbaf592de903fa29d4","observation_id":"9e8803f5-190a-4f4a-8fe8-b29929e45948","resolution":{"observed_at":"2026-08-11T11:43:09.786395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.771917Z","title":null,"venue":null,"work_id":"04a91513-f135-4a46-a40e-112b06d392e7","year":2017},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.710499Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:a5036d7551fb0038f82403b39ab2c0c7774d9866ba3b55dc7c48130421c4fc1b","observation_id":"733d4a51-0344-4ca1-b2b6-5c2ddd7d94ee","resolution":{"observed_at":"2026-08-11T11:43:09.775784Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.761094Z","title":"Quantification of uncertainties in biomed- ical image quantification 2021","venue":null,"work_id":"c17fdf30-ef9d-4607-9ef2-d8c6ef3ed6bf","year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.713732Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:7dc8fbdaae6dc42ac6933fec6edefefca2524d860c402520ddcc9a5fc7f1adf1","observation_id":"2f9d9d36-b19b-499f-af2f-0a6211228664","resolution":{"observed_at":"2026-08-11T11:43:09.764942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.749783Z","title":"The multimodal brain tumor image segmentation benchmark (brats)","venue":null,"work_id":"1a378d4a-3081-4ba1-adbe-51da612158d3","year":1993},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.717340Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:4572f42d3008df239bb4d2f649e4347a380aa651cbf510cd236cb536427da731","observation_id":"e54c87f7-d319-46b3-9bbe-2fc6a51bbdc7","resolution":{"observed_at":"2026-08-11T11:43:09.753751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.738340Z","title":"Ultrasound nerve segmentation, 2016","venue":null,"work_id":"840e32f3-9478-4a4f-b0af-be71712ee55c","year":2016},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.721641Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:b9b801493b34dc4dcf9eed0dcf3bc558a413ee0da765787385ba0b5469d0a5c2","observation_id":"69254e7b-c9b1-420d-b9af-0187535916af","resolution":{"observed_at":"2026-08-11T11:43:09.742114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.728536Z","title":"Adap- tivesam: Towards efficient tuning of sam for surgical scene segmentation","venue":null,"work_id":"cdf69d14-51e0-467d-b983-21bede477b21","year":2024},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.725115Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:4915802adc7554f43f3bbe04984c6193eeb3dde275d8cdfe8e765405c5696c88","observation_id":"256c7f12-86a9-4fda-ad7a-69156f4f2999","resolution":{"observed_at":"2026-08-11T11:43:09.731973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.719367Z","title":"An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset","venue":null,"work_id":"8fc729e0-b756-4ad1-ac42-017eb4d1377c","year":2021},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.728927Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:60b4d38acb6b3066afa543342bb86da1f72603a2c5836c2e28ab71eb20ebdd21","observation_id":"d8aa352f-2de2-4d8f-8ce0-b6525040b3b6","resolution":{"observed_at":"2026-08-11T11:43:09.722587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T11:43:09.709075Z","title":"An open access thy- roid ultrasound image database","venue":null,"work_id":"36086414-ab7c-48ae-b1e9-733a86dfd055","year":2015},"citing_paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T11:43:08.732680Z"},"links":{"citing_paper":"/paper/2412.15058"},"observation_digest":"sha256:8300b96fad86ad4bacec5a2b24a491a67e9b5221660c01fa2f31be5f15afdfc5","observation_id":"d6c48d6a-ffa7-4eb5-840b-c29504e01567","resolution":{"observed_at":"2026-08-11T11:43:09.712871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.15058","last_updated":"2025-08-31T22:28:41Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T11:38:06.592651Z","submitted_at":"2024-12-19T17:06:53Z","title":"MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":78,"verified_exact":4,"verified_fuzzy":17},"total_outbound_references":148},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 100 of 148 outbound references and 0 inbound Pith citation observations for arXiv:2412.15058."}