{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:U3YOOVJRYLE2PGANDQGBSUCSON","short_pith_number":"pith:U3YOOVJR","schema_version":"1.0","canonical_sha256":"a6f0e75531c2c9a7980d1c0c19505273457b8743a3a771ca2cb2ebfaf8861407","source":{"kind":"arxiv","id":"1901.07441","version":2},"attestation_state":"computed","paper":{"title":"PadChest: A large chest x-ray image dataset with multi-label annotated reports","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Antonio Pertusa, Aurelia Bustos, Jose-Maria Salinas, Maria de la Iglesia-Vay\\'a","submitted_at":"2019-01-22T16:04:27Z","abstract_excerpt":"We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical ta"},"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":"1901.07441","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-01-22T16:04:27Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"804eaffea77bf093157df2c81595c30ea38bc76568150b368a05c9753b0045c6","abstract_canon_sha256":"57ae416d419ef6cf6df127ce6f7009ad20acfc4143523c87b3803a50914ac315"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:31:20.756600Z","signature_b64":"FXam+p0XBC4izZK/jleg/Hdj+hqo4jAi2ILN5DLPUYzdEDO8vTYH7wII87fKIXgo6TxrJayf+xocbrkDV25nDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6f0e75531c2c9a7980d1c0c19505273457b8743a3a771ca2cb2ebfaf8861407","last_reissued_at":"2026-07-05T01:31:20.756098Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:31:20.756098Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PadChest: A large chest x-ray image dataset with multi-label annotated reports","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Antonio Pertusa, Aurelia Bustos, Jose-Maria Salinas, Maria de la Iglesia-Vay\\'a","submitted_at":"2019-01-22T16:04:27Z","abstract_excerpt":"We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical ta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.07441","kind":"arxiv","version":2},"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/1901.07441/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":"1901.07441","created_at":"2026-07-05T01:31:20.756169+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.07441v2","created_at":"2026-07-05T01:31:20.756169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.07441","created_at":"2026-07-05T01:31:20.756169+00:00"},{"alias_kind":"pith_short_12","alias_value":"U3YOOVJRYLE2","created_at":"2026-07-05T01:31:20.756169+00:00"},{"alias_kind":"pith_short_16","alias_value":"U3YOOVJRYLE2PGAN","created_at":"2026-07-05T01:31:20.756169+00:00"},{"alias_kind":"pith_short_8","alias_value":"U3YOOVJR","created_at":"2026-07-05T01:31:20.756169+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.19201","citing_title":"On-Device Continual Learning with Dual-Stage Buffer and Dynamic Loss for Point-of-Care Pneumonia Diagnosis","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON","json":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON.json","graph_json":"https://pith.science/api/pith-number/U3YOOVJRYLE2PGANDQGBSUCSON/graph.json","events_json":"https://pith.science/api/pith-number/U3YOOVJRYLE2PGANDQGBSUCSON/events.json","paper":"https://pith.science/paper/U3YOOVJR"},"agent_actions":{"view_html":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON","download_json":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON.json","view_paper":"https://pith.science/paper/U3YOOVJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.07441&json=true","fetch_graph":"https://pith.science/api/pith-number/U3YOOVJRYLE2PGANDQGBSUCSON/graph.json","fetch_events":"https://pith.science/api/pith-number/U3YOOVJRYLE2PGANDQGBSUCSON/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON/action/storage_attestation","attest_author":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON/action/author_attestation","sign_citation":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON/action/citation_signature","submit_replication":"https://pith.science/pith/U3YOOVJRYLE2PGANDQGBSUCSON/action/replication_record"}},"created_at":"2026-07-05T01:31:20.756169+00:00","updated_at":"2026-07-05T01:31:20.756169+00:00"}