{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LFKQC3S47HCINHCOIJGPXBTVAM","short_pith_number":"pith:LFKQC3S4","schema_version":"1.0","canonical_sha256":"5955016e5cf9c4869c4e424cfb8675031e8e6cbc20593660fb070f4a61e9e7f8","source":{"kind":"arxiv","id":"2311.17677","version":1},"attestation_state":"computed","paper":{"title":"COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexander Wong, Chi-en Amy Tai, Hayden Gunraj, Yifan Wu","submitted_at":"2023-11-29T14:40:31Z","abstract_excerpt":"The global ramifications of the COVID-19 pandemic remain significant, exerting persistent pressure on nations even three years after its initial outbreak. Deep learning models have shown promise in improving COVID-19 diagnostics but require diverse and larger-scale datasets to improve performance. In this paper, we introduce COVIDx CXR-4, an expanded multi-institutional open-source benchmark dataset for chest X-ray image-based computer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on the previous COVIDx CXR-3 dataset by increasing the total patient cohort size by greater than "},"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":"2311.17677","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-11-29T14:40:31Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"59bddadf1d062490f2ca3f52ce38bdaf24354aba16575313ef4e0cbe165177d1","abstract_canon_sha256":"34b3b0fa0a68b136eb4d261f70677e4eb64a902dbef7272ee9b6c36cf3436cce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:18:17.418977Z","signature_b64":"esP8fkDpRnmZFmEaOzAnJXnzV1aolaf4vq0jMffqIfwLULOigBeI5YEv8gaq4QWoIMi7QnGF8OYJqIN5HXTDAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5955016e5cf9c4869c4e424cfb8675031e8e6cbc20593660fb070f4a61e9e7f8","last_reissued_at":"2026-07-05T07:18:17.418479Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:18:17.418479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexander Wong, Chi-en Amy Tai, Hayden Gunraj, Yifan Wu","submitted_at":"2023-11-29T14:40:31Z","abstract_excerpt":"The global ramifications of the COVID-19 pandemic remain significant, exerting persistent pressure on nations even three years after its initial outbreak. Deep learning models have shown promise in improving COVID-19 diagnostics but require diverse and larger-scale datasets to improve performance. In this paper, we introduce COVIDx CXR-4, an expanded multi-institutional open-source benchmark dataset for chest X-ray image-based computer-aided COVID-19 diagnostics. COVIDx CXR-4 expands significantly on the previous COVIDx CXR-3 dataset by increasing the total patient cohort size by greater than "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.17677","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/2311.17677/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":"2311.17677","created_at":"2026-07-05T07:18:17.418537+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.17677v1","created_at":"2026-07-05T07:18:17.418537+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.17677","created_at":"2026-07-05T07:18:17.418537+00:00"},{"alias_kind":"pith_short_12","alias_value":"LFKQC3S47HCI","created_at":"2026-07-05T07:18:17.418537+00:00"},{"alias_kind":"pith_short_16","alias_value":"LFKQC3S47HCINHCO","created_at":"2026-07-05T07:18:17.418537+00:00"},{"alias_kind":"pith_short_8","alias_value":"LFKQC3S4","created_at":"2026-07-05T07:18:17.418537+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.21698","citing_title":"Adapting Foundation Vision-Language Models to Medical Diagnosis via Query-Driven Expert Bridging","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13756","citing_title":"MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging","ref_index":77,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM","json":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM.json","graph_json":"https://pith.science/api/pith-number/LFKQC3S47HCINHCOIJGPXBTVAM/graph.json","events_json":"https://pith.science/api/pith-number/LFKQC3S47HCINHCOIJGPXBTVAM/events.json","paper":"https://pith.science/paper/LFKQC3S4"},"agent_actions":{"view_html":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM","download_json":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM.json","view_paper":"https://pith.science/paper/LFKQC3S4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.17677&json=true","fetch_graph":"https://pith.science/api/pith-number/LFKQC3S47HCINHCOIJGPXBTVAM/graph.json","fetch_events":"https://pith.science/api/pith-number/LFKQC3S47HCINHCOIJGPXBTVAM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM/action/storage_attestation","attest_author":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM/action/author_attestation","sign_citation":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM/action/citation_signature","submit_replication":"https://pith.science/pith/LFKQC3S47HCINHCOIJGPXBTVAM/action/replication_record"}},"created_at":"2026-07-05T07:18:17.418537+00:00","updated_at":"2026-07-05T07:18:17.418537+00:00"}