{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LQK642QDY6MSTQIVU5CKPJJUEH","short_pith_number":"pith:LQK642QD","schema_version":"1.0","canonical_sha256":"5c15ee6a03c79929c115a744a7a53421e68fea3a9e127ab7af7326830c74e9ba","source":{"kind":"arxiv","id":"2306.11876","version":3},"attestation_state":"computed","paper":{"title":"BMAD: Benchmarks for Medical Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hanqiu Deng, Hanshi Sun, Jinan Bao, Xingyu Li, Yinsheng He, Zhaoxiang Zhang","submitted_at":"2023-06-20T20:23:46Z","abstract_excerpt":"Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for a"},"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":"2306.11876","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-06-20T20:23:46Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"085ccb9ca5f2fbc0115954fc0db24dc084ad1edf1412eb356429352aa83ecc8e","abstract_canon_sha256":"9d0b2f0e28b8e0aacb9d4c023f6c646dc5991e82035f62423189b9f4469537e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:12:43.712939Z","signature_b64":"WoDigHFzQN1alB2hV5wJFM5ZWo2/mZeOUeiPL8ImlpzGASM+7kIRYd2ETPSrh1LYe/yx56BRj8GKoJrvrMkcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c15ee6a03c79929c115a744a7a53421e68fea3a9e127ab7af7326830c74e9ba","last_reissued_at":"2026-07-05T08:12:43.712510Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:12:43.712510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BMAD: Benchmarks for Medical Anomaly Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hanqiu Deng, Hanshi Sun, Jinan Bao, Xingyu Li, Yinsheng He, Zhaoxiang Zhang","submitted_at":"2023-06-20T20:23:46Z","abstract_excerpt":"Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11876","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/2306.11876/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":"2306.11876","created_at":"2026-07-05T08:12:43.712568+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.11876v3","created_at":"2026-07-05T08:12:43.712568+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11876","created_at":"2026-07-05T08:12:43.712568+00:00"},{"alias_kind":"pith_short_12","alias_value":"LQK642QDY6MS","created_at":"2026-07-05T08:12:43.712568+00:00"},{"alias_kind":"pith_short_16","alias_value":"LQK642QDY6MSTQIV","created_at":"2026-07-05T08:12:43.712568+00:00"},{"alias_kind":"pith_short_8","alias_value":"LQK642QD","created_at":"2026-07-05T08:12:43.712568+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.18013","citing_title":"Towards Continual Visual Anomaly Detection in the Medical Domain","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH","json":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH.json","graph_json":"https://pith.science/api/pith-number/LQK642QDY6MSTQIVU5CKPJJUEH/graph.json","events_json":"https://pith.science/api/pith-number/LQK642QDY6MSTQIVU5CKPJJUEH/events.json","paper":"https://pith.science/paper/LQK642QD"},"agent_actions":{"view_html":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH","download_json":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH.json","view_paper":"https://pith.science/paper/LQK642QD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.11876&json=true","fetch_graph":"https://pith.science/api/pith-number/LQK642QDY6MSTQIVU5CKPJJUEH/graph.json","fetch_events":"https://pith.science/api/pith-number/LQK642QDY6MSTQIVU5CKPJJUEH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH/action/storage_attestation","attest_author":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH/action/author_attestation","sign_citation":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH/action/citation_signature","submit_replication":"https://pith.science/pith/LQK642QDY6MSTQIVU5CKPJJUEH/action/replication_record"}},"created_at":"2026-07-05T08:12:43.712568+00:00","updated_at":"2026-07-05T08:12:43.712568+00:00"}