{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7QO2Y7KSSTQM4VT6IAIEGMISTD","short_pith_number":"pith:7QO2Y7KS","schema_version":"1.0","canonical_sha256":"fc1dac7d5294e0ce567e401043311298e4966825972a4f4bc8db6763fb24e165","source":{"kind":"arxiv","id":"2501.14548","version":1},"attestation_state":"computed","paper":{"title":"Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianpeng Zhang, Le Lu, Ling Zhang, Lin Yang, Qi Zhang, Ruizhe Guo, Sinuo Wang, Tingbo Liang, Weiwei Cao, Xianghua Ye, Zhongyi Shui","submitted_at":"2025-01-24T14:50:48Z","abstract_excerpt":"Artificial intelligence (AI) shows great potential in assisting radiologists to improve the efficiency and accuracy of medical image interpretation and diagnosis. However, a versatile AI model requires large-scale data and comprehensive annotations, which are often impractical in medical settings. Recent studies leverage radiology reports as a naturally high-quality supervision for medical images, using contrastive language-image pre-training (CLIP) to develop language-informed models for radiological image interpretation. Nonetheless, these approaches typically contrast entire images with rep"},"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":"2501.14548","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T14:50:48Z","cross_cats_sorted":[],"title_canon_sha256":"56930ba88bae825b6f92968ef65709031f24a529cf7a89dbe2249a7aa81dd2a2","abstract_canon_sha256":"420cd2cc3ad76b7cf1b37dbafc7212645af9205b05c3892a8570e8bcbd72201a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:05:02.675143Z","signature_b64":"uE4mp8+w/buvs3bwF19piHZ+zqC1hdvCSPDvhhc2U62c5MVoXUdZ63JJWnGMhRSAVFfVbsQVaHDex1XACeSzAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fc1dac7d5294e0ce567e401043311298e4966825972a4f4bc8db6763fb24e165","last_reissued_at":"2026-07-05T10:05:02.674728Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:05:02.674728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianpeng Zhang, Le Lu, Ling Zhang, Lin Yang, Qi Zhang, Ruizhe Guo, Sinuo Wang, Tingbo Liang, Weiwei Cao, Xianghua Ye, Zhongyi Shui","submitted_at":"2025-01-24T14:50:48Z","abstract_excerpt":"Artificial intelligence (AI) shows great potential in assisting radiologists to improve the efficiency and accuracy of medical image interpretation and diagnosis. However, a versatile AI model requires large-scale data and comprehensive annotations, which are often impractical in medical settings. Recent studies leverage radiology reports as a naturally high-quality supervision for medical images, using contrastive language-image pre-training (CLIP) to develop language-informed models for radiological image interpretation. Nonetheless, these approaches typically contrast entire images with rep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14548","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/2501.14548/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":"2501.14548","created_at":"2026-07-05T10:05:02.674790+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.14548v1","created_at":"2026-07-05T10:05:02.674790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14548","created_at":"2026-07-05T10:05:02.674790+00:00"},{"alias_kind":"pith_short_12","alias_value":"7QO2Y7KSSTQM","created_at":"2026-07-05T10:05:02.674790+00:00"},{"alias_kind":"pith_short_16","alias_value":"7QO2Y7KSSTQM4VT6","created_at":"2026-07-05T10:05:02.674790+00:00"},{"alias_kind":"pith_short_8","alias_value":"7QO2Y7KS","created_at":"2026-07-05T10:05:02.674790+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.13544","citing_title":"CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05460","citing_title":"ORACLE-CT: Anatomy-Aware Support Pooling for CT Classification","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08787","citing_title":"Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13544","citing_title":"CA-GCL: Cross-Anatomy Global-Local Contrastive Learning for Robust 3D Medical Image Understanding","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13813","citing_title":"JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08787","citing_title":"Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Models","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10437","citing_title":"Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD","json":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD.json","graph_json":"https://pith.science/api/pith-number/7QO2Y7KSSTQM4VT6IAIEGMISTD/graph.json","events_json":"https://pith.science/api/pith-number/7QO2Y7KSSTQM4VT6IAIEGMISTD/events.json","paper":"https://pith.science/paper/7QO2Y7KS"},"agent_actions":{"view_html":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD","download_json":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD.json","view_paper":"https://pith.science/paper/7QO2Y7KS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.14548&json=true","fetch_graph":"https://pith.science/api/pith-number/7QO2Y7KSSTQM4VT6IAIEGMISTD/graph.json","fetch_events":"https://pith.science/api/pith-number/7QO2Y7KSSTQM4VT6IAIEGMISTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/action/storage_attestation","attest_author":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/action/author_attestation","sign_citation":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/action/citation_signature","submit_replication":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/action/replication_record"}},"created_at":"2026-07-05T10:05:02.674790+00:00","updated_at":"2026-07-05T10:05:02.674790+00:00"}