{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ILANTPEVMW5Z2KGH7BDNTLNYFT","short_pith_number":"pith:ILANTPEV","schema_version":"1.0","canonical_sha256":"42c0d9bc9565bb9d28c7f846d9adb82cec81b9b529afa412cdf1807f53c26651","source":{"kind":"arxiv","id":"2501.09001","version":2},"attestation_state":"computed","paper":{"title":"Vision Foundation Models for Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Andriy Fedorov, Benjamin H. Kann, Dennis Bontempi, Hugo J. W. L. Aerts, Ibrahim Hadzic, Keno Bressem, Raymond H. Mak, Suraj Pai","submitted_at":"2025-01-15T18:30:58Z","abstract_excerpt":"Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (CT) scans from the Imaging Data Commons through label-agnostic contrastive learning. We evaluated CT-FM across four categories of tasks, namely, whole-body and tumor segmentation, head CT triage, medical image retrieval, and semantic understanding, showing superior performance aga"},"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":"2501.09001","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-01-15T18:30:58Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"33c26039e32aa3ff1f5508d5128c9849b37aa31a34dceeca49ed2dc8ffd26ce1","abstract_canon_sha256":"8be3ebd021c56b3cb12a82eb753531dfca00b49e58ba8499d6600037446d3614"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:20:18.715232Z","signature_b64":"Bp9oaJbMjZ89zj8aKUuuImhebcBal7Tct0RRjSR+bSrG9wB3he+J9YJhJjInaurtqb5CNUCFqBYhODr9N+d9AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42c0d9bc9565bb9d28c7f846d9adb82cec81b9b529afa412cdf1807f53c26651","last_reissued_at":"2026-07-05T10:20:18.714524Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:20:18.714524Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Vision Foundation Models for Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Andriy Fedorov, Benjamin H. Kann, Dennis Bontempi, Hugo J. W. L. Aerts, Ibrahim Hadzic, Keno Bressem, Raymond H. Mak, Suraj Pai","submitted_at":"2025-01-15T18:30:58Z","abstract_excerpt":"Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (CT) scans from the Imaging Data Commons through label-agnostic contrastive learning. We evaluated CT-FM across four categories of tasks, namely, whole-body and tumor segmentation, head CT triage, medical image retrieval, and semantic understanding, showing superior performance aga"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09001","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/2501.09001/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":"2501.09001","created_at":"2026-07-05T10:20:18.714597+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.09001v2","created_at":"2026-07-05T10:20:18.714597+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09001","created_at":"2026-07-05T10:20:18.714597+00:00"},{"alias_kind":"pith_short_12","alias_value":"ILANTPEVMW5Z","created_at":"2026-07-05T10:20:18.714597+00:00"},{"alias_kind":"pith_short_16","alias_value":"ILANTPEVMW5Z2KGH","created_at":"2026-07-05T10:20:18.714597+00:00"},{"alias_kind":"pith_short_8","alias_value":"ILANTPEV","created_at":"2026-07-05T10:20:18.714597+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07775","citing_title":"DALE-CT: Depth-Aware Foundation Models for Computed Tomography","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21906","citing_title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21906","citing_title":"Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2509.10784","citing_title":"Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13813","citing_title":"JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04133","citing_title":"Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00448","citing_title":"Learning from Compressed CT: Feature Attention Style Transfer and Structured Factorized Projections for Resource-Efficient Medical Image Analysis","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07749","citing_title":"Benchmarking Foundation Models for Renal Lesion Stratification in CT","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT","json":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT.json","graph_json":"https://pith.science/api/pith-number/ILANTPEVMW5Z2KGH7BDNTLNYFT/graph.json","events_json":"https://pith.science/api/pith-number/ILANTPEVMW5Z2KGH7BDNTLNYFT/events.json","paper":"https://pith.science/paper/ILANTPEV"},"agent_actions":{"view_html":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT","download_json":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT.json","view_paper":"https://pith.science/paper/ILANTPEV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.09001&json=true","fetch_graph":"https://pith.science/api/pith-number/ILANTPEVMW5Z2KGH7BDNTLNYFT/graph.json","fetch_events":"https://pith.science/api/pith-number/ILANTPEVMW5Z2KGH7BDNTLNYFT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT/action/storage_attestation","attest_author":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT/action/author_attestation","sign_citation":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT/action/citation_signature","submit_replication":"https://pith.science/pith/ILANTPEVMW5Z2KGH7BDNTLNYFT/action/replication_record"}},"created_at":"2026-07-05T10:20:18.714597+00:00","updated_at":"2026-07-05T10:20:18.714597+00:00"}