{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QGVAREIMGC77I2OCGZ23SCYKGX","short_pith_number":"pith:QGVAREIM","schema_version":"1.0","canonical_sha256":"81aa08910c30bff469c23675b90b0a35d5242bf53113d78208dd7f0260290c18","source":{"kind":"arxiv","id":"2012.15029","version":3},"attestation_state":"computed","paper":{"title":"VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Anh T. Nguyen, Au D. Hoang, Binh T. Nguyen, Chi M. Pham, Cuong D. Do, Cuong N. Nguyen, Dat Q. Tran, Dat T. Ngo, Diep H. Dinh, Dung B. Nguyen, Dung D. Le, Hang T. T. Tong, Ha Q. Nguyen, Hien N. Phan, Hieu H. Pham, Khanh Lam, Linh T. Le, Luu T. Doan, Minh Dao, Nghia T. Nguyen, Nhan T. Nguyen, Phuong H. Ho, Que V. Nguyen, Van Vu","submitted_at":"2020-12-30T04:08:00Z","abstract_excerpt":"Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels"},"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":"2012.15029","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2020-12-30T04:08:00Z","cross_cats_sorted":[],"title_canon_sha256":"1e8e813c663d547fc13cba747828e16c308eb241ca2cbcd5aec08e0bd4fd16c3","abstract_canon_sha256":"a0ee6e4e682cf01edc429b15d3b2a631f583ad442ef00b8122f9082b8b03c2f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:41.017172Z","signature_b64":"SkQiOMCVsuz8JSlqlv83VH/AGSlW39LWpbrDhRcMQFr1RfOjuhTQ6O4Dw/C6HD8DlDIwhVFheosQCl8UXepbAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81aa08910c30bff469c23675b90b0a35d5242bf53113d78208dd7f0260290c18","last_reissued_at":"2026-07-05T04:06:41.016697Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:41.016697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Anh T. Nguyen, Au D. Hoang, Binh T. Nguyen, Chi M. Pham, Cuong D. Do, Cuong N. Nguyen, Dat Q. Tran, Dat T. Ngo, Diep H. Dinh, Dung B. Nguyen, Dung D. Le, Hang T. T. Tong, Ha Q. Nguyen, Hien N. Phan, Hieu H. Pham, Khanh Lam, Linh T. Le, Luu T. Doan, Minh Dao, Nghia T. Nguyen, Nhan T. Nguyen, Phuong H. Ho, Que V. Nguyen, Van Vu","submitted_at":"2020-12-30T04:08:00Z","abstract_excerpt":"Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.15029","kind":"arxiv","version":3},"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/2012.15029/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":"2012.15029","created_at":"2026-07-05T04:06:41.016756+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.15029v3","created_at":"2026-07-05T04:06:41.016756+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.15029","created_at":"2026-07-05T04:06:41.016756+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGVAREIMGC77","created_at":"2026-07-05T04:06:41.016756+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGVAREIMGC77I2OC","created_at":"2026-07-05T04:06:41.016756+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGVAREIM","created_at":"2026-07-05T04:06:41.016756+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2408.16213","citing_title":"M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation","ref_index":34,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX","json":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX.json","graph_json":"https://pith.science/api/pith-number/QGVAREIMGC77I2OCGZ23SCYKGX/graph.json","events_json":"https://pith.science/api/pith-number/QGVAREIMGC77I2OCGZ23SCYKGX/events.json","paper":"https://pith.science/paper/QGVAREIM"},"agent_actions":{"view_html":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX","download_json":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX.json","view_paper":"https://pith.science/paper/QGVAREIM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.15029&json=true","fetch_graph":"https://pith.science/api/pith-number/QGVAREIMGC77I2OCGZ23SCYKGX/graph.json","fetch_events":"https://pith.science/api/pith-number/QGVAREIMGC77I2OCGZ23SCYKGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX/action/storage_attestation","attest_author":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX/action/author_attestation","sign_citation":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX/action/citation_signature","submit_replication":"https://pith.science/pith/QGVAREIMGC77I2OCGZ23SCYKGX/action/replication_record"}},"created_at":"2026-07-05T04:06:41.016756+00:00","updated_at":"2026-07-05T04:06:41.016756+00:00"}