{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:YA7G5KUVKH57XR5XTEHL2WKGX4","short_pith_number":"pith:YA7G5KUV","schema_version":"1.0","canonical_sha256":"c03e6eaa9551fbfbc7b7990ebd5946bf1d0e6cff1c8929794c489417c14c0e50","source":{"kind":"arxiv","id":"2303.14153","version":1},"attestation_state":"computed","paper":{"title":"Local Contrastive Learning for Medical Image Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"R. Tang, S. A. Rizvi, X. Hu, X. Jiang, X. Ma","submitted_at":"2023-03-24T17:04:26Z","abstract_excerpt":"The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Regi"},"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":"2303.14153","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-24T17:04:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8eec4f026f05a503ecf557d50e6e842374e3f9e33145c2df677677cea7756be4","abstract_canon_sha256":"4eed45f693a7108c0d9b1a0401b06c349f85cd9deabebd624418173049977adb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:24.260809Z","signature_b64":"3r3ZqORuDk3/F6ri5Fu1Xb8wJ738AjZJ+z2KNIKoqvzqRPIqVBPO7QZx/k8BzAJU9t2kSph0TctjirncW3UZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c03e6eaa9551fbfbc7b7990ebd5946bf1d0e6cff1c8929794c489417c14c0e50","last_reissued_at":"2026-07-05T05:54:24.260381Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:24.260381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Local Contrastive Learning for Medical Image Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"R. Tang, S. A. Rizvi, X. Hu, X. Jiang, X. Ma","submitted_at":"2023-03-24T17:04:26Z","abstract_excerpt":"The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Regi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.14153","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/2303.14153/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":"2303.14153","created_at":"2026-07-05T05:54:24.260447+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.14153v1","created_at":"2026-07-05T05:54:24.260447+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.14153","created_at":"2026-07-05T05:54:24.260447+00:00"},{"alias_kind":"pith_short_12","alias_value":"YA7G5KUVKH57","created_at":"2026-07-05T05:54:24.260447+00:00"},{"alias_kind":"pith_short_16","alias_value":"YA7G5KUVKH57XR5X","created_at":"2026-07-05T05:54:24.260447+00:00"},{"alias_kind":"pith_short_8","alias_value":"YA7G5KUV","created_at":"2026-07-05T05:54:24.260447+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4","json":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4.json","graph_json":"https://pith.science/api/pith-number/YA7G5KUVKH57XR5XTEHL2WKGX4/graph.json","events_json":"https://pith.science/api/pith-number/YA7G5KUVKH57XR5XTEHL2WKGX4/events.json","paper":"https://pith.science/paper/YA7G5KUV"},"agent_actions":{"view_html":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4","download_json":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4.json","view_paper":"https://pith.science/paper/YA7G5KUV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.14153&json=true","fetch_graph":"https://pith.science/api/pith-number/YA7G5KUVKH57XR5XTEHL2WKGX4/graph.json","fetch_events":"https://pith.science/api/pith-number/YA7G5KUVKH57XR5XTEHL2WKGX4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4/action/storage_attestation","attest_author":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4/action/author_attestation","sign_citation":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4/action/citation_signature","submit_replication":"https://pith.science/pith/YA7G5KUVKH57XR5XTEHL2WKGX4/action/replication_record"}},"created_at":"2026-07-05T05:54:24.260447+00:00","updated_at":"2026-07-05T05:54:24.260447+00:00"}