{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TTIFKN3TLRC47BTBLWXX25XQRD","short_pith_number":"pith:TTIFKN3T","schema_version":"1.0","canonical_sha256":"9cd05537735c45cf86615daf7d76f088dc6f4bb36282e79658114be1dda25e9f","source":{"kind":"arxiv","id":"2408.02676","version":2},"attestation_state":"computed","paper":{"title":"On Biases in a UK Biobank-based Retinal Image Classification Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.CY","eess.IV"],"primary_cat":"cs.LG","authors_text":"Anissa Alloula, Bart{\\l}omiej W. Papie\\.z, Daniel R McGowan, Rima Mustafa","submitted_at":"2024-07-30T10:50:07Z","abstract_excerpt":"Recent work has uncovered alarming disparities in the performance of machine learning models in healthcare. In this study, we explore whether such disparities are present in the UK Biobank fundus retinal images by training and evaluating a disease classification model on these images. We assess possible disparities across various population groups and find substantial differences despite strong overall performance of the model. In particular, we discover unfair performance for certain assessment centres, which is surprising given the rigorous data standardisation protocol. We compare how these"},"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":"2408.02676","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-07-30T10:50:07Z","cross_cats_sorted":["cs.AI","cs.CV","cs.CY","eess.IV"],"title_canon_sha256":"3c8ed534b124bd20f2c859e273e8cdcc766d67a5ad42255ea989ee393b7d53b4","abstract_canon_sha256":"79fad1a1dbe36c77920b496e0e9ae64507c7207edcc40fad2cf144efae223cc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:25:46.213837Z","signature_b64":"rp+0mHtRfqiCHyTdYAabpiBuWnM4ciPVTtB/d/ipkHGuzXQtElacU0VKp/1qjsr94MNtIYFt+NfnqF+Zpe3PDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9cd05537735c45cf86615daf7d76f088dc6f4bb36282e79658114be1dda25e9f","last_reissued_at":"2026-07-05T09:25:46.213332Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:25:46.213332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Biases in a UK Biobank-based Retinal Image Classification Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.CY","eess.IV"],"primary_cat":"cs.LG","authors_text":"Anissa Alloula, Bart{\\l}omiej W. Papie\\.z, Daniel R McGowan, Rima Mustafa","submitted_at":"2024-07-30T10:50:07Z","abstract_excerpt":"Recent work has uncovered alarming disparities in the performance of machine learning models in healthcare. In this study, we explore whether such disparities are present in the UK Biobank fundus retinal images by training and evaluating a disease classification model on these images. We assess possible disparities across various population groups and find substantial differences despite strong overall performance of the model. In particular, we discover unfair performance for certain assessment centres, which is surprising given the rigorous data standardisation protocol. We compare how these"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.02676","kind":"arxiv","version":2},"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/2408.02676/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":"2408.02676","created_at":"2026-07-05T09:25:46.213388+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.02676v2","created_at":"2026-07-05T09:25:46.213388+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.02676","created_at":"2026-07-05T09:25:46.213388+00:00"},{"alias_kind":"pith_short_12","alias_value":"TTIFKN3TLRC4","created_at":"2026-07-05T09:25:46.213388+00:00"},{"alias_kind":"pith_short_16","alias_value":"TTIFKN3TLRC47BTB","created_at":"2026-07-05T09:25:46.213388+00:00"},{"alias_kind":"pith_short_8","alias_value":"TTIFKN3T","created_at":"2026-07-05T09:25:46.213388+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.11753","citing_title":"Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD","json":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD.json","graph_json":"https://pith.science/api/pith-number/TTIFKN3TLRC47BTBLWXX25XQRD/graph.json","events_json":"https://pith.science/api/pith-number/TTIFKN3TLRC47BTBLWXX25XQRD/events.json","paper":"https://pith.science/paper/TTIFKN3T"},"agent_actions":{"view_html":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD","download_json":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD.json","view_paper":"https://pith.science/paper/TTIFKN3T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.02676&json=true","fetch_graph":"https://pith.science/api/pith-number/TTIFKN3TLRC47BTBLWXX25XQRD/graph.json","fetch_events":"https://pith.science/api/pith-number/TTIFKN3TLRC47BTBLWXX25XQRD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD/action/storage_attestation","attest_author":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD/action/author_attestation","sign_citation":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD/action/citation_signature","submit_replication":"https://pith.science/pith/TTIFKN3TLRC47BTBLWXX25XQRD/action/replication_record"}},"created_at":"2026-07-05T09:25:46.213388+00:00","updated_at":"2026-07-05T09:25:46.213388+00:00"}