{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FNNZO5Z57DOJ66QKN37YUZRMQ6","short_pith_number":"pith:FNNZO5Z5","canonical_record":{"source":{"id":"2504.20837","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-29T15:00:25Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4c303f4a6730578c2f0092a5d865f4030634e12a1c170596caf5e3179a888074","abstract_canon_sha256":"6910e7055ebaff3f65473410110b491c1114d1195455aa237ecb3a4a433bed1e"},"schema_version":"1.0"},"canonical_sha256":"2b5b97773df8dc9f7a0a6eff8a662c87a15a7064f1f820dfdefe5931279dc17e","source":{"kind":"arxiv","id":"2504.20837","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.20837","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"arxiv_version","alias_value":"2504.20837v1","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.20837","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"pith_short_12","alias_value":"FNNZO5Z57DOJ","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"pith_short_16","alias_value":"FNNZO5Z57DOJ66QK","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"pith_short_8","alias_value":"FNNZO5Z5","created_at":"2026-07-05T10:55:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FNNZO5Z57DOJ66QKN37YUZRMQ6","target":"record","payload":{"canonical_record":{"source":{"id":"2504.20837","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-29T15:00:25Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4c303f4a6730578c2f0092a5d865f4030634e12a1c170596caf5e3179a888074","abstract_canon_sha256":"6910e7055ebaff3f65473410110b491c1114d1195455aa237ecb3a4a433bed1e"},"schema_version":"1.0"},"canonical_sha256":"2b5b97773df8dc9f7a0a6eff8a662c87a15a7064f1f820dfdefe5931279dc17e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:55:49.664192Z","signature_b64":"JlvBPVa2p7DbO75kx6/C7K3ACwdP4Q34zPpAIsLtwhZU/4URaoJ+qiqNFkEXAShbv7NUo84pbO83Xxk5X4GrAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b5b97773df8dc9f7a0a6eff8a662c87a15a7064f1f820dfdefe5931279dc17e","last_reissued_at":"2026-07-05T10:55:49.663693Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:55:49.663693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.20837","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":"2026-07-05T10:55:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WW2J3Bsem19dNRSQ5xxaoSsyRUbnFe8AHzSboZ09zb+Q7xNTIrtmJZUGNLxpUZWGswPYsBp+/b1xUzs2Go7wBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:26:01.074269Z"},"content_sha256":"728c64590e49fc4112dd24b883fa2a96fb6d4e43831eefb0316f99f02226d21c","schema_version":"1.0","event_id":"sha256:728c64590e49fc4112dd24b883fa2a96fb6d4e43831eefb0316f99f02226d21c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FNNZO5Z57DOJ66QKN37YUZRMQ6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RadSAM: Segmenting 3D radiological images with a 2D promptable model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Corentin Dancette, Daniel Tordjman, Elodie Ferreres, H\\'el\\`ene Philippe, Julien Khlaut, Pierre Manceron, Tom Boeken","submitted_at":"2025-04-29T15:00:25Z","abstract_excerpt":"Medical image segmentation is a crucial and time-consuming task in clinical care, where mask precision is extremely important. The Segment Anything Model (SAM) offers a promising approach, as it provides an interactive interface based on visual prompting and edition to refine an initial segmentation. This model has strong generalization capabilities, does not rely on predefined classes, and adapts to diverse objects; however, it is pre-trained on natural images and lacks the ability to process medical data effectively. In addition, this model is built for 2D images, whereas a whole medical dom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.20837","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/2504.20837/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":"2026-07-05T10:55:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"recPM8x0ECVZ1F566xBeP9EDtpiHX8H2wv2F8tNd/JbeeXcQvaoI+7ZxY9iJIzZ3kx5GtC969YX+HfiQkhM/AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T23:26:01.074857Z"},"content_sha256":"dafc4a2766e1a20b7d786768cdedb00178df47dff4fb3e05b22ce204b836c675","schema_version":"1.0","event_id":"sha256:dafc4a2766e1a20b7d786768cdedb00178df47dff4fb3e05b22ce204b836c675"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FNNZO5Z57DOJ66QKN37YUZRMQ6/bundle.json","state_url":"https://pith.science/pith/FNNZO5Z57DOJ66QKN37YUZRMQ6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FNNZO5Z57DOJ66QKN37YUZRMQ6/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":"2026-08-19T23:26:01Z","links":{"resolver":"https://pith.science/pith/FNNZO5Z57DOJ66QKN37YUZRMQ6","bundle":"https://pith.science/pith/FNNZO5Z57DOJ66QKN37YUZRMQ6/bundle.json","state":"https://pith.science/pith/FNNZO5Z57DOJ66QKN37YUZRMQ6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FNNZO5Z57DOJ66QKN37YUZRMQ6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FNNZO5Z57DOJ66QKN37YUZRMQ6","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":"6910e7055ebaff3f65473410110b491c1114d1195455aa237ecb3a4a433bed1e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-29T15:00:25Z","title_canon_sha256":"4c303f4a6730578c2f0092a5d865f4030634e12a1c170596caf5e3179a888074"},"schema_version":"1.0","source":{"id":"2504.20837","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.20837","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"arxiv_version","alias_value":"2504.20837v1","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.20837","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"pith_short_12","alias_value":"FNNZO5Z57DOJ","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"pith_short_16","alias_value":"FNNZO5Z57DOJ66QK","created_at":"2026-07-05T10:55:49Z"},{"alias_kind":"pith_short_8","alias_value":"FNNZO5Z5","created_at":"2026-07-05T10:55:49Z"}],"graph_snapshots":[{"event_id":"sha256:dafc4a2766e1a20b7d786768cdedb00178df47dff4fb3e05b22ce204b836c675","target":"graph","created_at":"2026-07-05T10:55:49Z","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/2504.20837/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical image segmentation is a crucial and time-consuming task in clinical care, where mask precision is extremely important. The Segment Anything Model (SAM) offers a promising approach, as it provides an interactive interface based on visual prompting and edition to refine an initial segmentation. This model has strong generalization capabilities, does not rely on predefined classes, and adapts to diverse objects; however, it is pre-trained on natural images and lacks the ability to process medical data effectively. In addition, this model is built for 2D images, whereas a whole medical dom","authors_text":"Corentin Dancette, Daniel Tordjman, Elodie Ferreres, H\\'el\\`ene Philippe, Julien Khlaut, Pierre Manceron, Tom Boeken","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-29T15:00:25Z","title":"RadSAM: Segmenting 3D radiological images with a 2D promptable model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.20837","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":"sha256:728c64590e49fc4112dd24b883fa2a96fb6d4e43831eefb0316f99f02226d21c","target":"record","created_at":"2026-07-05T10:55:49Z","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":"6910e7055ebaff3f65473410110b491c1114d1195455aa237ecb3a4a433bed1e","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-29T15:00:25Z","title_canon_sha256":"4c303f4a6730578c2f0092a5d865f4030634e12a1c170596caf5e3179a888074"},"schema_version":"1.0","source":{"id":"2504.20837","kind":"arxiv","version":1}},"canonical_sha256":"2b5b97773df8dc9f7a0a6eff8a662c87a15a7064f1f820dfdefe5931279dc17e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b5b97773df8dc9f7a0a6eff8a662c87a15a7064f1f820dfdefe5931279dc17e","first_computed_at":"2026-07-05T10:55:49.663693Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:55:49.663693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JlvBPVa2p7DbO75kx6/C7K3ACwdP4Q34zPpAIsLtwhZU/4URaoJ+qiqNFkEXAShbv7NUo84pbO83Xxk5X4GrAw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:55:49.664192Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.20837","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:728c64590e49fc4112dd24b883fa2a96fb6d4e43831eefb0316f99f02226d21c","sha256:dafc4a2766e1a20b7d786768cdedb00178df47dff4fb3e05b22ce204b836c675"],"state_sha256":"97f203360cb212297095bdea14172b4f9812ce4992001166291bb08360ddec13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1ZTPSwTW1RTaUu6Evlw7HHLOWtEMkN/BYbNEYjSVf6jFKihik4s2qYrEFUaLsynDNoQcM7LGaAbbI6AFJgdIDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T23:26:01.080288Z","bundle_sha256":"dee14b4e1e76ce46e2a7dc25af156364f7bead923a780765f7d3e48cbbe95586"}}