{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GHYW5ZEEKNAY6VT6TXCJPZBQYG","short_pith_number":"pith:GHYW5ZEE","schema_version":"1.0","canonical_sha256":"31f16ee48453418f567e9dc497e430c19d27f4b7f74b2fea64ceeb51c6b3f676","source":{"kind":"arxiv","id":"2312.02366","version":4},"attestation_state":"computed","paper":{"title":"Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Abdulrhman Aljouie, Jiahong Ouyang, Mohammed Baharoon, Waseem Qureshi, Wei Peng, Yanwu Xu","submitted_at":"2023-12-04T21:47:10Z","abstract_excerpt":"The integration of deep learning systems into healthcare has been hindered by the resource-intensive process of data annotation and the inability of these systems to generalize to different data distributions. Foundation models, which are models pre-trained on large datasets, have emerged as a solution to reduce reliance on annotated data and enhance model generalizability and robustness. DINOv2 is an open-source foundation model pre-trained with self-supervised learning on 142 million curated natural images that exhibits promising capabilities across various vision tasks. Nevertheless, a crit"},"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":"2312.02366","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-04T21:47:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"18924c631473d30b9a36c3df3230f38e0673ad68d3f0589eb34666e75909b463","abstract_canon_sha256":"29a1905c6e4b81e6ca337709d553b228709117eabbc195f45d57a9e943533464"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:06:56.395266Z","signature_b64":"eCqfrkRcQmDgP5GE5Yu85e20Pbsa6Hlj7zw44l+N2vejtYl2GQhkV4fMYXXUlSzWfl4wZlf0MSDexQ00+rhzCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31f16ee48453418f567e9dc497e430c19d27f4b7f74b2fea64ceeb51c6b3f676","last_reissued_at":"2026-07-05T09:06:56.394834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:06:56.394834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Abdulrhman Aljouie, Jiahong Ouyang, Mohammed Baharoon, Waseem Qureshi, Wei Peng, Yanwu Xu","submitted_at":"2023-12-04T21:47:10Z","abstract_excerpt":"The integration of deep learning systems into healthcare has been hindered by the resource-intensive process of data annotation and the inability of these systems to generalize to different data distributions. Foundation models, which are models pre-trained on large datasets, have emerged as a solution to reduce reliance on annotated data and enhance model generalizability and robustness. DINOv2 is an open-source foundation model pre-trained with self-supervised learning on 142 million curated natural images that exhibits promising capabilities across various vision tasks. Nevertheless, a crit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02366","kind":"arxiv","version":4},"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/2312.02366/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":"2312.02366","created_at":"2026-07-05T09:06:56.394885+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.02366v4","created_at":"2026-07-05T09:06:56.394885+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02366","created_at":"2026-07-05T09:06:56.394885+00:00"},{"alias_kind":"pith_short_12","alias_value":"GHYW5ZEEKNAY","created_at":"2026-07-05T09:06:56.394885+00:00"},{"alias_kind":"pith_short_16","alias_value":"GHYW5ZEEKNAY6VT6","created_at":"2026-07-05T09:06:56.394885+00:00"},{"alias_kind":"pith_short_8","alias_value":"GHYW5ZEE","created_at":"2026-07-05T09:06:56.394885+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17989","citing_title":"Recover Semantics First, Generate Better: Improved Latent Modeling for 3D MRI Reconstruction and Cross-Contrast Synthesis","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07775","citing_title":"DALE-CT: Depth-Aware Foundation Models for Computed Tomography","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20337","citing_title":"Capability $\\neq$ Interpretability: Human Interpretability of Vision Foundation Models","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13798","citing_title":"VoxCor: Training-Free Volumetric Features for Multimodal Voxel Correspondence","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22557","citing_title":"Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21977","citing_title":"Euclid Quick Data Release (Q1). AstroVink: A vision transformer approach to find strong gravitational lens systems","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10609","citing_title":"Self-supervised Pretraining of Cell Segmentation Models","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG","json":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG.json","graph_json":"https://pith.science/api/pith-number/GHYW5ZEEKNAY6VT6TXCJPZBQYG/graph.json","events_json":"https://pith.science/api/pith-number/GHYW5ZEEKNAY6VT6TXCJPZBQYG/events.json","paper":"https://pith.science/paper/GHYW5ZEE"},"agent_actions":{"view_html":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG","download_json":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG.json","view_paper":"https://pith.science/paper/GHYW5ZEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.02366&json=true","fetch_graph":"https://pith.science/api/pith-number/GHYW5ZEEKNAY6VT6TXCJPZBQYG/graph.json","fetch_events":"https://pith.science/api/pith-number/GHYW5ZEEKNAY6VT6TXCJPZBQYG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG/action/storage_attestation","attest_author":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG/action/author_attestation","sign_citation":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG/action/citation_signature","submit_replication":"https://pith.science/pith/GHYW5ZEEKNAY6VT6TXCJPZBQYG/action/replication_record"}},"created_at":"2026-07-05T09:06:56.394885+00:00","updated_at":"2026-07-05T09:06:56.394885+00:00"}