{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2SFHEVP7ALUALI2UB5LIAR6OHD","short_pith_number":"pith:2SFHEVP7","schema_version":"1.0","canonical_sha256":"d48a7255ff02e805a3540f568047ce38ed1bf884710870a91ec18babed1814f5","source":{"kind":"arxiv","id":"2405.19097","version":4},"attestation_state":"computed","paper":{"title":"A study of why we need to reassess full reference image quality assessment with medical images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"AIX-COVNET Collaboration, Ander Biguri, Anna Breger, Carola-Bibiane Sch\\\"onlieb, Clemens Karner, Elisabeth Brunner, Ian Selby, Janek Gr\\\"ohl, Lipeng Ning, Malena Sabat\\'e Landman, Michael Roberts, Nicole Amberg, Sepideh Hatamikia, S\\\"oren Dittmer","submitted_at":"2024-05-29T14:01:40Z","abstract_excerpt":"Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on medical images. The popular full reference (FR) IQA measures PSNR and SSIM are known and tested for working successfully in many natural imaging tasks, but discrepancies in medical scenarios have been reported in the literature, highlighting the gap between development and actual clinical application. Such inconsistencies are not surprising, as medical images have very different properties than natural images, and PSNR"},"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":"2405.19097","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2024-05-29T14:01:40Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"630dd14cff6ce332b9085e8dadeec88db44d4d2719169c7046eae2cd76309d45","abstract_canon_sha256":"1034655aa6e7dac42e2146452eb0761f2abb78d265c686a68794c436488ed44e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:31:23.211918Z","signature_b64":"RRptlYje/gdoSYGM2VWC9YQr/d3jCTXKBMxcFQlP7Pj76lYXhy24ZDbtQ4nnAP7bkbxpl09qxhvYTQzSt9T8CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d48a7255ff02e805a3540f568047ce38ed1bf884710870a91ec18babed1814f5","last_reissued_at":"2026-07-05T10:31:23.211274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:31:23.211274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A study of why we need to reassess full reference image quality assessment with medical images","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"AIX-COVNET Collaboration, Ander Biguri, Anna Breger, Carola-Bibiane Sch\\\"onlieb, Clemens Karner, Elisabeth Brunner, Ian Selby, Janek Gr\\\"ohl, Lipeng Ning, Malena Sabat\\'e Landman, Michael Roberts, Nicole Amberg, Sepideh Hatamikia, S\\\"oren Dittmer","submitted_at":"2024-05-29T14:01:40Z","abstract_excerpt":"Image quality assessment (IQA) is indispensable in clinical practice to ensure high standards, as well as in the development stage of machine learning algorithms that operate on medical images. The popular full reference (FR) IQA measures PSNR and SSIM are known and tested for working successfully in many natural imaging tasks, but discrepancies in medical scenarios have been reported in the literature, highlighting the gap between development and actual clinical application. Such inconsistencies are not surprising, as medical images have very different properties than natural images, and PSNR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.19097","kind":"arxiv","version":4},"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/2405.19097/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":"2405.19097","created_at":"2026-07-05T10:31:23.211347+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.19097v4","created_at":"2026-07-05T10:31:23.211347+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.19097","created_at":"2026-07-05T10:31:23.211347+00:00"},{"alias_kind":"pith_short_12","alias_value":"2SFHEVP7ALUA","created_at":"2026-07-05T10:31:23.211347+00:00"},{"alias_kind":"pith_short_16","alias_value":"2SFHEVP7ALUALI2U","created_at":"2026-07-05T10:31:23.211347+00:00"},{"alias_kind":"pith_short_8","alias_value":"2SFHEVP7","created_at":"2026-07-05T10:31:23.211347+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.12624","citing_title":"Pathology-Guided Virtual Staining Metric for Evaluation and Training","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD","json":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD.json","graph_json":"https://pith.science/api/pith-number/2SFHEVP7ALUALI2UB5LIAR6OHD/graph.json","events_json":"https://pith.science/api/pith-number/2SFHEVP7ALUALI2UB5LIAR6OHD/events.json","paper":"https://pith.science/paper/2SFHEVP7"},"agent_actions":{"view_html":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD","download_json":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD.json","view_paper":"https://pith.science/paper/2SFHEVP7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.19097&json=true","fetch_graph":"https://pith.science/api/pith-number/2SFHEVP7ALUALI2UB5LIAR6OHD/graph.json","fetch_events":"https://pith.science/api/pith-number/2SFHEVP7ALUALI2UB5LIAR6OHD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD/action/storage_attestation","attest_author":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD/action/author_attestation","sign_citation":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD/action/citation_signature","submit_replication":"https://pith.science/pith/2SFHEVP7ALUALI2UB5LIAR6OHD/action/replication_record"}},"created_at":"2026-07-05T10:31:23.211347+00:00","updated_at":"2026-07-05T10:31:23.211347+00:00"}