{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LDPITVK2NQ6UAKLBMJ72VLZVQ2","short_pith_number":"pith:LDPITVK2","schema_version":"1.0","canonical_sha256":"58de89d55a6c3d402961627faaaf35868f76f6789cb09814587e21c7e99fe02b","source":{"kind":"arxiv","id":"2606.22556","version":1},"attestation_state":"computed","paper":{"title":"HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiayu Huo, Jingyuan Hong, Le Zhang, Liyun Chen, Meng Zhou","submitted_at":"2026-06-21T15:26:48Z","abstract_excerpt":"Anomaly detection is essential for medical image analysis, where pathological regions often appear as rare deviations from normal anatomical structures. While training-based methods have achieved promising performance, they require task-specific optimization and extensive normal data, which limits scalability across modalities and institutions. Training-free approaches offer greater flexibility by leveraging pretrained visual representations, yet existing methods typically rely on simple nearest-neighbor retrieval and naive aggregation strategies, which may fail to capture hierarchical semanti"},"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":"2606.22556","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-21T15:26:48Z","cross_cats_sorted":[],"title_canon_sha256":"15fe0e4291b915a131827e10fd2aa76d85473cef4e4813224e003a4c9680a0c1","abstract_canon_sha256":"fb246e7dc14b1295f3a09a6c55e782aec605a325e63106c7df3106937d9329e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:41.478362Z","signature_b64":"LF0CF9NnvLIH9Q1TGqwl6ucZvJGNhL0e4b9rV2zFURXM1yva9kKNWoMg9Sol+l+n+6xgRFxqPJAJUiyzbSiBCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58de89d55a6c3d402961627faaaf35868f76f6789cb09814587e21c7e99fe02b","last_reissued_at":"2026-06-23T02:13:41.477970Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:41.477970Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiayu Huo, Jingyuan Hong, Le Zhang, Liyun Chen, Meng Zhou","submitted_at":"2026-06-21T15:26:48Z","abstract_excerpt":"Anomaly detection is essential for medical image analysis, where pathological regions often appear as rare deviations from normal anatomical structures. While training-based methods have achieved promising performance, they require task-specific optimization and extensive normal data, which limits scalability across modalities and institutions. Training-free approaches offer greater flexibility by leveraging pretrained visual representations, yet existing methods typically rely on simple nearest-neighbor retrieval and naive aggregation strategies, which may fail to capture hierarchical semanti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.22556","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/2606.22556/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":"2606.22556","created_at":"2026-06-23T02:13:41.478033+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.22556v1","created_at":"2026-06-23T02:13:41.478033+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.22556","created_at":"2026-06-23T02:13:41.478033+00:00"},{"alias_kind":"pith_short_12","alias_value":"LDPITVK2NQ6U","created_at":"2026-06-23T02:13:41.478033+00:00"},{"alias_kind":"pith_short_16","alias_value":"LDPITVK2NQ6UAKLB","created_at":"2026-06-23T02:13:41.478033+00:00"},{"alias_kind":"pith_short_8","alias_value":"LDPITVK2","created_at":"2026-06-23T02:13:41.478033+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/LDPITVK2NQ6UAKLBMJ72VLZVQ2","json":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2.json","graph_json":"https://pith.science/api/pith-number/LDPITVK2NQ6UAKLBMJ72VLZVQ2/graph.json","events_json":"https://pith.science/api/pith-number/LDPITVK2NQ6UAKLBMJ72VLZVQ2/events.json","paper":"https://pith.science/paper/LDPITVK2"},"agent_actions":{"view_html":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2","download_json":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2.json","view_paper":"https://pith.science/paper/LDPITVK2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.22556&json=true","fetch_graph":"https://pith.science/api/pith-number/LDPITVK2NQ6UAKLBMJ72VLZVQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/LDPITVK2NQ6UAKLBMJ72VLZVQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2/action/storage_attestation","attest_author":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2/action/author_attestation","sign_citation":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2/action/citation_signature","submit_replication":"https://pith.science/pith/LDPITVK2NQ6UAKLBMJ72VLZVQ2/action/replication_record"}},"created_at":"2026-06-23T02:13:41.478033+00:00","updated_at":"2026-06-23T02:13:41.478033+00:00"}