{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:7JKUSPUYMCE3RWF3R436J3YCN2","short_pith_number":"pith:7JKUSPUY","schema_version":"1.0","canonical_sha256":"fa55493e986089b8d8bb8f37e4ef026e8cd7044686031e8230eb00699706a1ca","source":{"kind":"arxiv","id":"2002.11379","version":2},"attestation_state":"computed","paper":{"title":"CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Amirhossein Kiani, Andrew Y. Ng, Anirudh Joshi, Anuj Pareek, Jeremy Irvin, Matthew P. Lungren, Phil Chen, Pranav Rajpurkar","submitted_at":"2020-02-26T09:44:21Z","abstract_excerpt":"Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translation of chest x-ray algorithms to the clinical setting. We examine the performance of the top 10 performing models on the CheXpert challenge leaderboard on three tasks: (1) TB detection, (2) pathology detection on photos of chest x-rays, and (3) pathology detection on data from an external institution. First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0.851 on the 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":"2002.11379","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2020-02-26T09:44:21Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"e0fe777f5c78e350e35339e50087a56e2b20eed483059d520ad2014e99843a16","abstract_canon_sha256":"01d9d8807f8d95b7646e9a7244f59be8e9e6e14e3fd4f2ca350595dd56726b58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:47:18.969953Z","signature_b64":"QNFXQQGV8kDrsutQHy8Z6WMnL4Jj7LC7fOxwfyNPRfDyamG2Zk9W/wUwO9r/xZ1oyheePYRgLrzVQxKPxmc+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa55493e986089b8d8bb8f37e4ef026e8cd7044686031e8230eb00699706a1ca","last_reissued_at":"2026-07-05T00:47:18.969535Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:47:18.969535Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Amirhossein Kiani, Andrew Y. Ng, Anirudh Joshi, Anuj Pareek, Jeremy Irvin, Matthew P. Lungren, Phil Chen, Pranav Rajpurkar","submitted_at":"2020-02-26T09:44:21Z","abstract_excerpt":"Although there have been several recent advances in the application of deep learning algorithms to chest x-ray interpretation, we identify three major challenges for the translation of chest x-ray algorithms to the clinical setting. We examine the performance of the top 10 performing models on the CheXpert challenge leaderboard on three tasks: (1) TB detection, (2) pathology detection on photos of chest x-rays, and (3) pathology detection on data from an external institution. First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0.851 on the ta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.11379","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/2002.11379/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":"2002.11379","created_at":"2026-07-05T00:47:18.969599+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.11379v2","created_at":"2026-07-05T00:47:18.969599+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.11379","created_at":"2026-07-05T00:47:18.969599+00:00"},{"alias_kind":"pith_short_12","alias_value":"7JKUSPUYMCE3","created_at":"2026-07-05T00:47:18.969599+00:00"},{"alias_kind":"pith_short_16","alias_value":"7JKUSPUYMCE3RWF3","created_at":"2026-07-05T00:47:18.969599+00:00"},{"alias_kind":"pith_short_8","alias_value":"7JKUSPUY","created_at":"2026-07-05T00:47:18.969599+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/7JKUSPUYMCE3RWF3R436J3YCN2","json":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2.json","graph_json":"https://pith.science/api/pith-number/7JKUSPUYMCE3RWF3R436J3YCN2/graph.json","events_json":"https://pith.science/api/pith-number/7JKUSPUYMCE3RWF3R436J3YCN2/events.json","paper":"https://pith.science/paper/7JKUSPUY"},"agent_actions":{"view_html":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2","download_json":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2.json","view_paper":"https://pith.science/paper/7JKUSPUY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.11379&json=true","fetch_graph":"https://pith.science/api/pith-number/7JKUSPUYMCE3RWF3R436J3YCN2/graph.json","fetch_events":"https://pith.science/api/pith-number/7JKUSPUYMCE3RWF3R436J3YCN2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2/action/storage_attestation","attest_author":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2/action/author_attestation","sign_citation":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2/action/citation_signature","submit_replication":"https://pith.science/pith/7JKUSPUYMCE3RWF3R436J3YCN2/action/replication_record"}},"created_at":"2026-07-05T00:47:18.969599+00:00","updated_at":"2026-07-05T00:47:18.969599+00:00"}