{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WMAISEPXT4BGST2QR75HN7LBLN","short_pith_number":"pith:WMAISEPX","schema_version":"1.0","canonical_sha256":"b3008911f79f02694f508ffa76fd615b435026926792c05e2c4614cf2e54c9e5","source":{"kind":"arxiv","id":"2005.01577","version":1},"attestation_state":"computed","paper":{"title":"COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Jianhua Yao, Junzhou Huang, Mingkui Tan, Peilin Zhao, Qingyao Wu, Shuaicheng Niu, Yifan Zhang, Ying Wei, Zhen Qiu","submitted_at":"2020-04-30T03:13:40Z","abstract_excerpt":"The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to screen chest radiography images, e.g., X-Ray or CT images. However, such examinations are time-consuming and labor-intensive, leading to limited diagnostic efficiency. To solve this issue, AI-based technologies, such as deep learning, have been used recently as effective computer-aided means to improve diagnostic efficiency. However, one practical and criti"},"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":"2005.01577","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-04-30T03:13:40Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"9dcb218fef0ddee943b130d3fb4b6c1fbbc3bf38f5ae62bfe14833acc9827fc2","abstract_canon_sha256":"cd50675587f54a3ec7c030daa94035334a30f7579966416128c84eac351975b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:00:09.153668Z","signature_b64":"/TFw1psYZwG5IpCnGZc4N7nDYnid5tB/YLC2XX2fGPwR8/EXkLJg0JSefBfBFUaQ3cvV6AKLhVPDGX+/D/RhCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3008911f79f02694f508ffa76fd615b435026926792c05e2c4614cf2e54c9e5","last_reissued_at":"2026-07-05T01:00:09.153309Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:00:09.153309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Jianhua Yao, Junzhou Huang, Mingkui Tan, Peilin Zhao, Qingyao Wu, Shuaicheng Niu, Yifan Zhang, Ying Wei, Zhen Qiu","submitted_at":"2020-04-30T03:13:40Z","abstract_excerpt":"The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to screen chest radiography images, e.g., X-Ray or CT images. However, such examinations are time-consuming and labor-intensive, leading to limited diagnostic efficiency. To solve this issue, AI-based technologies, such as deep learning, have been used recently as effective computer-aided means to improve diagnostic efficiency. However, one practical and criti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.01577","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/2005.01577/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":"2005.01577","created_at":"2026-07-05T01:00:09.153375+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.01577v1","created_at":"2026-07-05T01:00:09.153375+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.01577","created_at":"2026-07-05T01:00:09.153375+00:00"},{"alias_kind":"pith_short_12","alias_value":"WMAISEPXT4BG","created_at":"2026-07-05T01:00:09.153375+00:00"},{"alias_kind":"pith_short_16","alias_value":"WMAISEPXT4BGST2Q","created_at":"2026-07-05T01:00:09.153375+00:00"},{"alias_kind":"pith_short_8","alias_value":"WMAISEPX","created_at":"2026-07-05T01:00:09.153375+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2411.05824","citing_title":"Navigating Distribution Shifts in Medical Image Analysis: A Survey","ref_index":243,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN","json":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN.json","graph_json":"https://pith.science/api/pith-number/WMAISEPXT4BGST2QR75HN7LBLN/graph.json","events_json":"https://pith.science/api/pith-number/WMAISEPXT4BGST2QR75HN7LBLN/events.json","paper":"https://pith.science/paper/WMAISEPX"},"agent_actions":{"view_html":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN","download_json":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN.json","view_paper":"https://pith.science/paper/WMAISEPX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.01577&json=true","fetch_graph":"https://pith.science/api/pith-number/WMAISEPXT4BGST2QR75HN7LBLN/graph.json","fetch_events":"https://pith.science/api/pith-number/WMAISEPXT4BGST2QR75HN7LBLN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN/action/storage_attestation","attest_author":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN/action/author_attestation","sign_citation":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN/action/citation_signature","submit_replication":"https://pith.science/pith/WMAISEPXT4BGST2QR75HN7LBLN/action/replication_record"}},"created_at":"2026-07-05T01:00:09.153375+00:00","updated_at":"2026-07-05T01:00:09.153375+00:00"}