{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BYD7DPR2QGWBZUVNZMVNPX7I4G","short_pith_number":"pith:BYD7DPR2","schema_version":"1.0","canonical_sha256":"0e07f1be3a81ac1cd2adcb2ad7dfe8e1b082cb1151ddba2db8eb0d37b8d166dc","source":{"kind":"arxiv","id":"2511.09588","version":3},"attestation_state":"computed","paper":{"title":"Diffusion-Based Quality Control of Medical Image Segmentations across Organs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"eess.IV","authors_text":"Hava Chaptoukaev, Maria A. Zuluaga, Michela Antonelli, M. Jorge Cardoso, S\\'ebastien Ourselin, Vincenzo Marcian\\`o, Virginia Fernandez","submitted_at":"2025-11-12T13:24:53Z","abstract_excerpt":"Medical image segmentation using deep learning (DL) has enabled the development of automated analysis pipelines for large-scale population studies. However, state-of-the-art DL methods are prone to hallucinations, which can result in anatomically implausible segmentations. With manual correction impractical at scale, automated quality control (QC) techniques have to address the challenge. While promising, existing QC methods are organ-specific, limiting their generalizability and usability beyond their original intended task. To overcome this limitation, we propose no-new Quality Control (nnQC"},"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":"2511.09588","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-11-12T13:24:53Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"70fad724f6f0883762c63d8db0420b3d043561e6f6fe02fa8b9647223d86b844","abstract_canon_sha256":"f27a8e9db34fb1f021b887afefde9b73c1767f62d2814a709e5c47ffbe350ae0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:22:02.118448Z","signature_b64":"MM5NIhGsH5BwDnKtHfAcSabwuhnlDrvgi+ZIGUbCDN4iVc189Vv5sfBwK8+zaNuTPhbmCN1ffy+9HRQkbQrDAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e07f1be3a81ac1cd2adcb2ad7dfe8e1b082cb1151ddba2db8eb0d37b8d166dc","last_reissued_at":"2026-07-14T01:22:02.117428Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:22:02.117428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diffusion-Based Quality Control of Medical Image Segmentations across Organs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"eess.IV","authors_text":"Hava Chaptoukaev, Maria A. Zuluaga, Michela Antonelli, M. Jorge Cardoso, S\\'ebastien Ourselin, Vincenzo Marcian\\`o, Virginia Fernandez","submitted_at":"2025-11-12T13:24:53Z","abstract_excerpt":"Medical image segmentation using deep learning (DL) has enabled the development of automated analysis pipelines for large-scale population studies. However, state-of-the-art DL methods are prone to hallucinations, which can result in anatomically implausible segmentations. With manual correction impractical at scale, automated quality control (QC) techniques have to address the challenge. While promising, existing QC methods are organ-specific, limiting their generalizability and usability beyond their original intended task. To overcome this limitation, we propose no-new Quality Control (nnQC"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.09588","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/2511.09588/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":"2511.09588","created_at":"2026-07-14T01:22:02.117879+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.09588v3","created_at":"2026-07-14T01:22:02.117879+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.09588","created_at":"2026-07-14T01:22:02.117879+00:00"},{"alias_kind":"pith_short_12","alias_value":"BYD7DPR2QGWB","created_at":"2026-07-14T01:22:02.117879+00:00"},{"alias_kind":"pith_short_16","alias_value":"BYD7DPR2QGWBZUVN","created_at":"2026-07-14T01:22:02.117879+00:00"},{"alias_kind":"pith_short_8","alias_value":"BYD7DPR2","created_at":"2026-07-14T01:22:02.117879+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/BYD7DPR2QGWBZUVNZMVNPX7I4G","json":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G.json","graph_json":"https://pith.science/api/pith-number/BYD7DPR2QGWBZUVNZMVNPX7I4G/graph.json","events_json":"https://pith.science/api/pith-number/BYD7DPR2QGWBZUVNZMVNPX7I4G/events.json","paper":"https://pith.science/paper/BYD7DPR2"},"agent_actions":{"view_html":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G","download_json":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G.json","view_paper":"https://pith.science/paper/BYD7DPR2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.09588&json=true","fetch_graph":"https://pith.science/api/pith-number/BYD7DPR2QGWBZUVNZMVNPX7I4G/graph.json","fetch_events":"https://pith.science/api/pith-number/BYD7DPR2QGWBZUVNZMVNPX7I4G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G/action/storage_attestation","attest_author":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G/action/author_attestation","sign_citation":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G/action/citation_signature","submit_replication":"https://pith.science/pith/BYD7DPR2QGWBZUVNZMVNPX7I4G/action/replication_record"}},"created_at":"2026-07-14T01:22:02.117879+00:00","updated_at":"2026-07-14T01:22:02.117879+00:00"}