{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QOGISDXLUXMUANK42EVQEV7OS4","short_pith_number":"pith:QOGISDXL","schema_version":"1.0","canonical_sha256":"838c890eeba5d940355cd12b0257ee970712917726996c423eb5fdf7bb3d1f01","source":{"kind":"arxiv","id":"2507.11569","version":2},"attestation_state":"computed","paper":{"title":"Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Hanxue Gu, Maciej A. Mazurowski, Nicholas Konz, Qihang Li, Yaqian Chen","submitted_at":"2025-07-15T00:17:14Z","abstract_excerpt":"Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. However, their performance has mostly been tested in the context of rigid or less complex structures, such as the brain or abdominal organs, and it remains unclear whether these models can handle more challenging, deformable anatomy. Breast MRI registration is particularly difficult due to significant anatomical variation between patients, deformation caused by patient positioning, and the presence of thin and complex inte"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.11569","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-07-15T00:17:14Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"19549f65327e666fa0ab9aa31816bfd724cff8bdd179916c04e3bbed31052660","abstract_canon_sha256":"0a05ccd9e771e051a6a83191f1c6f71d6acc35f213b4dbfec5d64d6970b14e28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:51:00.202088Z","signature_b64":"uBP0AjjFBBoG9of2ha5Aw9lB4BtRfDT1ul1tync3oknDm+GCj/4gv5qFmIXkIQvuVCvlbCC+nyy0Dl7C1DPbCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"838c890eeba5d940355cd12b0257ee970712917726996c423eb5fdf7bb3d1f01","last_reissued_at":"2026-07-05T11:51:00.201518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:51:00.201518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Hanxue Gu, Maciej A. Mazurowski, Nicholas Konz, Qihang Li, Yaqian Chen","submitted_at":"2025-07-15T00:17:14Z","abstract_excerpt":"Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. However, their performance has mostly been tested in the context of rigid or less complex structures, such as the brain or abdominal organs, and it remains unclear whether these models can handle more challenging, deformable anatomy. Breast MRI registration is particularly difficult due to significant anatomical variation between patients, deformation caused by patient positioning, and the presence of thin and complex inte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.11569","kind":"arxiv","version":2},"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/2507.11569/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.11569","created_at":"2026-07-05T11:51:00.201583+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.11569v2","created_at":"2026-07-05T11:51:00.201583+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.11569","created_at":"2026-07-05T11:51:00.201583+00:00"},{"alias_kind":"pith_short_12","alias_value":"QOGISDXLUXMU","created_at":"2026-07-05T11:51:00.201583+00:00"},{"alias_kind":"pith_short_16","alias_value":"QOGISDXLUXMUANK4","created_at":"2026-07-05T11:51:00.201583+00:00"},{"alias_kind":"pith_short_8","alias_value":"QOGISDXL","created_at":"2026-07-05T11:51:00.201583+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.13798","citing_title":"VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4","json":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4.json","graph_json":"https://pith.science/api/pith-number/QOGISDXLUXMUANK42EVQEV7OS4/graph.json","events_json":"https://pith.science/api/pith-number/QOGISDXLUXMUANK42EVQEV7OS4/events.json","paper":"https://pith.science/paper/QOGISDXL"},"agent_actions":{"view_html":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4","download_json":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4.json","view_paper":"https://pith.science/paper/QOGISDXL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.11569&json=true","fetch_graph":"https://pith.science/api/pith-number/QOGISDXLUXMUANK42EVQEV7OS4/graph.json","fetch_events":"https://pith.science/api/pith-number/QOGISDXLUXMUANK42EVQEV7OS4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4/action/storage_attestation","attest_author":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4/action/author_attestation","sign_citation":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4/action/citation_signature","submit_replication":"https://pith.science/pith/QOGISDXLUXMUANK42EVQEV7OS4/action/replication_record"}},"created_at":"2026-07-05T11:51:00.201583+00:00","updated_at":"2026-07-05T11:51:00.201583+00:00"}