{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:7QO2Y7KSSTQM4VT6IAIEGMISTD","short_pith_number":"pith:7QO2Y7KS","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"},"canonical_sha256":"fc1dac7d5294e0ce567e401043311298e4966825972a4f4bc8db6763fb24e165","source":{"kind":"arxiv","id":"2501.14548","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14548","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14548v1","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14548","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"pith_short_12","alias_value":"7QO2Y7KSSTQM","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"pith_short_16","alias_value":"7QO2Y7KSSTQM4VT6","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"pith_short_8","alias_value":"7QO2Y7KS","created_at":"2026-07-05T10:05:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:7QO2Y7KSSTQM4VT6IAIEGMISTD","target":"record","payload":{"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"},"canonical_sha256":"fc1dac7d5294e0ce567e401043311298e4966825972a4f4bc8db6763fb24e165","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"},"source_kind":"arxiv","source_id":"2501.14548","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":"2026-07-05T10:05:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"feqBgZ9OZPLCP9XFMWvZghiBMfAha/g1EByD4L/A+V32NBRKK318kfZ45ou20RPBK2nvKilHtxk04Jc4XpU2AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:12:37.442670Z"},"content_sha256":"2c8867eecd3e26ef1f03736071666d6c8dec6bbd590b0d2856a936c312421255","schema_version":"1.0","event_id":"sha256:2c8867eecd3e26ef1f03736071666d6c8dec6bbd590b0d2856a936c312421255"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:7QO2Y7KSSTQM4VT6IAIEGMISTD","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:05:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JWxwtwZgNrxs7qraYfXCY0i67XOAp0WrdGp2aIGyL4ZvhcLoV8/axJPYC7Y0mr+cgmFY9dQXotzaukS3JNvzBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T00:12:37.443505Z"},"content_sha256":"44b9afa22c6f4194d93bb2af4b836f7a32f5978f27d0d7a95f910f8a44662d26","schema_version":"1.0","event_id":"sha256:44b9afa22c6f4194d93bb2af4b836f7a32f5978f27d0d7a95f910f8a44662d26"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/bundle.json","state_url":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/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":"2026-08-05T00:12:37Z","links":{"resolver":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD","bundle":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/bundle.json","state":"https://pith.science/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7QO2Y7KSSTQM4VT6IAIEGMISTD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7QO2Y7KSSTQM4VT6IAIEGMISTD","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":"420cd2cc3ad76b7cf1b37dbafc7212645af9205b05c3892a8570e8bcbd72201a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T14:50:48Z","title_canon_sha256":"56930ba88bae825b6f92968ef65709031f24a529cf7a89dbe2249a7aa81dd2a2"},"schema_version":"1.0","source":{"id":"2501.14548","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14548","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14548v1","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14548","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"pith_short_12","alias_value":"7QO2Y7KSSTQM","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"pith_short_16","alias_value":"7QO2Y7KSSTQM4VT6","created_at":"2026-07-05T10:05:02Z"},{"alias_kind":"pith_short_8","alias_value":"7QO2Y7KS","created_at":"2026-07-05T10:05:02Z"}],"graph_snapshots":[{"event_id":"sha256:44b9afa22c6f4194d93bb2af4b836f7a32f5978f27d0d7a95f910f8a44662d26","target":"graph","created_at":"2026-07-05T10:05:02Z","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/2501.14548/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Jianpeng Zhang, Le Lu, Ling Zhang, Lin Yang, Qi Zhang, Ruizhe Guo, Sinuo Wang, Tingbo Liang, Weiwei Cao, Xianghua Ye, Zhongyi Shui","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T14:50:48Z","title":"Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14548","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":"sha256:2c8867eecd3e26ef1f03736071666d6c8dec6bbd590b0d2856a936c312421255","target":"record","created_at":"2026-07-05T10:05:02Z","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":"420cd2cc3ad76b7cf1b37dbafc7212645af9205b05c3892a8570e8bcbd72201a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-24T14:50:48Z","title_canon_sha256":"56930ba88bae825b6f92968ef65709031f24a529cf7a89dbe2249a7aa81dd2a2"},"schema_version":"1.0","source":{"id":"2501.14548","kind":"arxiv","version":1}},"canonical_sha256":"fc1dac7d5294e0ce567e401043311298e4966825972a4f4bc8db6763fb24e165","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fc1dac7d5294e0ce567e401043311298e4966825972a4f4bc8db6763fb24e165","first_computed_at":"2026-07-05T10:05:02.674728Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:05:02.674728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uE4mp8+w/buvs3bwF19piHZ+zqC1hdvCSPDvhhc2U62c5MVoXUdZ63JJWnGMhRSAVFfVbsQVaHDex1XACeSzAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:05:02.675143Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14548","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2c8867eecd3e26ef1f03736071666d6c8dec6bbd590b0d2856a936c312421255","sha256:44b9afa22c6f4194d93bb2af4b836f7a32f5978f27d0d7a95f910f8a44662d26"],"state_sha256":"c2b8363464adb69510a7e4d4da5e3307568c09a9b1ff5a38c5f19d7b115ec8ae"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"liuY6jvf4GtBPpA57JUEP79EqdhLP33sgtbgsEgiOvMPG+shnle7E9RcavQ7leoIVQ7AsBiEUePvsRGIyDJPCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T00:12:37.451715Z","bundle_sha256":"3a79cd1d2d0a8ef6b2e429228ddb6d99c89da6a343303f7f21bed45aa9491208"}}