{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3U7ERY2GW7DQAU7YL7YXH7R3CE","short_pith_number":"pith:3U7ERY2G","canonical_record":{"source":{"id":"2304.06131","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-12T19:36:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"954a2f80915828674d102b55061d1410a5418d059cd20caac7fe11196dc6b636","abstract_canon_sha256":"8e538d832de25dfbd4da8e691fe3bd7fb645d4ace2c7fa88b0e48549c2f974b0"},"schema_version":"1.0"},"canonical_sha256":"dd3e48e346b7c70053f85ff173fe3b112505058abe3bb83e67023501127eea39","source":{"kind":"arxiv","id":"2304.06131","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.06131","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"arxiv_version","alias_value":"2304.06131v1","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.06131","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"pith_short_12","alias_value":"3U7ERY2GW7DQ","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"pith_short_16","alias_value":"3U7ERY2GW7DQAU7Y","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"pith_short_8","alias_value":"3U7ERY2G","created_at":"2026-07-05T06:00:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3U7ERY2GW7DQAU7YL7YXH7R3CE","target":"record","payload":{"canonical_record":{"source":{"id":"2304.06131","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-12T19:36:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"954a2f80915828674d102b55061d1410a5418d059cd20caac7fe11196dc6b636","abstract_canon_sha256":"8e538d832de25dfbd4da8e691fe3bd7fb645d4ace2c7fa88b0e48549c2f974b0"},"schema_version":"1.0"},"canonical_sha256":"dd3e48e346b7c70053f85ff173fe3b112505058abe3bb83e67023501127eea39","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:00:38.928726Z","signature_b64":"yA0/GYyREhOWbsFJmtNUN7EUwQcCafc97GiZ4ez68MXsE94uqbPFnhXlZ2K2At26h3CKpgTSh8SaHjLIw8A0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dd3e48e346b7c70053f85ff173fe3b112505058abe3bb83e67023501127eea39","last_reissued_at":"2026-07-05T06:00:38.928181Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:00:38.928181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.06131","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:00:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"05FGR7KYS9TWGj/5SQ5hgR3/S1lVpvQlv7PcI/45RRI7QbBpMzdN/3M4rr7H2jampi93ulFdquVHNVu8a2t0Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:10:37.407596Z"},"content_sha256":"cba2ad93ba7592da0e0dca3a0eb43636db6b69aadc0c8fd3e1896b8fa57c1b02","schema_version":"1.0","event_id":"sha256:cba2ad93ba7592da0e0dca3a0eb43636db6b69aadc0c8fd3e1896b8fa57c1b02"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3U7ERY2GW7DQAU7YL7YXH7R3CE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UniverSeg: Universal Medical Image Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Adrian V. Dalca, John Guttag, Jose Javier Gonzalez Ortiz, Mert R. Sabuncu, Tianyu Ma, Victor Ion Butoi","submitted_at":"2023-04-12T19:36:46Z","abstract_excerpt":"While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.06131","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.06131/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:00:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fy4Qnv/t2Ut8LBaIu0AdJYSQz80qpEmb4MlcOoLkY3+DBDsXmY89xOGXYr+VzQKiKCAFNiifrhjhMjIlQVbgBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:10:37.408723Z"},"content_sha256":"944939907ab2420242cafee91f2a2ea74f9df8e9c54a7027ad38a09de421e44e","schema_version":"1.0","event_id":"sha256:944939907ab2420242cafee91f2a2ea74f9df8e9c54a7027ad38a09de421e44e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3U7ERY2GW7DQAU7YL7YXH7R3CE/bundle.json","state_url":"https://pith.science/pith/3U7ERY2GW7DQAU7YL7YXH7R3CE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3U7ERY2GW7DQAU7YL7YXH7R3CE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-13T04:10:37Z","links":{"resolver":"https://pith.science/pith/3U7ERY2GW7DQAU7YL7YXH7R3CE","bundle":"https://pith.science/pith/3U7ERY2GW7DQAU7YL7YXH7R3CE/bundle.json","state":"https://pith.science/pith/3U7ERY2GW7DQAU7YL7YXH7R3CE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3U7ERY2GW7DQAU7YL7YXH7R3CE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3U7ERY2GW7DQAU7YL7YXH7R3CE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8e538d832de25dfbd4da8e691fe3bd7fb645d4ace2c7fa88b0e48549c2f974b0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-12T19:36:46Z","title_canon_sha256":"954a2f80915828674d102b55061d1410a5418d059cd20caac7fe11196dc6b636"},"schema_version":"1.0","source":{"id":"2304.06131","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.06131","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"arxiv_version","alias_value":"2304.06131v1","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.06131","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"pith_short_12","alias_value":"3U7ERY2GW7DQ","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"pith_short_16","alias_value":"3U7ERY2GW7DQAU7Y","created_at":"2026-07-05T06:00:38Z"},{"alias_kind":"pith_short_8","alias_value":"3U7ERY2G","created_at":"2026-07-05T06:00:38Z"}],"graph_snapshots":[{"event_id":"sha256:944939907ab2420242cafee91f2a2ea74f9df8e9c54a7027ad38a09de421e44e","target":"graph","created_at":"2026-07-05T06:00:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2304.06131/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example","authors_text":"Adrian V. Dalca, John Guttag, Jose Javier Gonzalez Ortiz, Mert R. Sabuncu, Tianyu Ma, Victor Ion Butoi","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-12T19:36:46Z","title":"UniverSeg: Universal Medical Image Segmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.06131","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cba2ad93ba7592da0e0dca3a0eb43636db6b69aadc0c8fd3e1896b8fa57c1b02","target":"record","created_at":"2026-07-05T06:00:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8e538d832de25dfbd4da8e691fe3bd7fb645d4ace2c7fa88b0e48549c2f974b0","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-12T19:36:46Z","title_canon_sha256":"954a2f80915828674d102b55061d1410a5418d059cd20caac7fe11196dc6b636"},"schema_version":"1.0","source":{"id":"2304.06131","kind":"arxiv","version":1}},"canonical_sha256":"dd3e48e346b7c70053f85ff173fe3b112505058abe3bb83e67023501127eea39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dd3e48e346b7c70053f85ff173fe3b112505058abe3bb83e67023501127eea39","first_computed_at":"2026-07-05T06:00:38.928181Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:00:38.928181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yA0/GYyREhOWbsFJmtNUN7EUwQcCafc97GiZ4ez68MXsE94uqbPFnhXlZ2K2At26h3CKpgTSh8SaHjLIw8A0DA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:00:38.928726Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.06131","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cba2ad93ba7592da0e0dca3a0eb43636db6b69aadc0c8fd3e1896b8fa57c1b02","sha256:944939907ab2420242cafee91f2a2ea74f9df8e9c54a7027ad38a09de421e44e"],"state_sha256":"9770d5628dd637989cb72832a1e980f7fa7ad107e5520d33ba50a0e49d762e64"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HhKrGC4giYLj+UDvr17aECVAHMgvwwT+Hr/QnQ1pRrUbtTvvlJACa0W8cxS4/nbXpnRlolMmADXiUWQcKaI8BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T04:10:37.414589Z","bundle_sha256":"e95ca7cece1a8eafc60f8faaed42df2ce6bb60bf157f6a5df4a6bfcf1fc335d5"}}