eprintid: 10200673 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/20/06/73 datestamp: 2024-11-26 13:18:02 lastmod: 2024-11-26 13:18:02 status_changed: 2024-11-26 13:18:02 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: McCabe, J creators_name: Cheng, D creators_name: Bhamani, A creators_name: Mullin, M creators_name: Patrick, T creators_name: Nair, A creators_name: Janes, SM creators_name: Sudre, CH creators_name: Jacob, J title: Exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithms ispublished: pub divisions: UCL divisions: B02 divisions: C10 divisions: D17 divisions: D14 divisions: K71 divisions: GA3 divisions: G17 keywords: Nodule Detection Algorithms, Fairness in AI, Lung Cancer Screening note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. abstract: Lung cancer is the leading cause of cancer mortality worldwide. Asymptomatic in its early stages, it is disproportionately detected when the disease is advanced. Resource constraints have resulted in increasing reliance on computer-aided detection (CADe) systems to assist with scan evaluation. The datasets used to train these algorithms are often unbalanced in their representation of protected groups e.g. sex and ethnicity. This project investigates whether there are performance disparities in detecting clinically relevant nodules across under-represented groups in selected, state-of-the-art nodule detection algorithms trained on data from a screening program in the UK. Our analysis revealed that overall, the algorithms demonstrate equitable performance across various demographic groups. However, their performance varies strongly across nodule characteristics (size and type) in line with their prevalence in the training set. To ensure continued equitable performance, algorithms should not only consider demographic but also nodule attributes representativeness in their training. date: 2024-10-13 date_type: published publisher: Springer, Cham official_url: http://dx.doi.org/10.1007/978-3-031-72787-0_8 full_text_type: other language: eng verified: verified_manual elements_id: 2333472 doi: 10.1007/978-3-031-72787-0_8 isbn_13: 9783031727863 lyricists_name: Sudre, Carole lyricists_name: Janes, Samuel lyricists_name: Jacob, Joseph lyricists_name: Cheng, Daryl lyricists_name: Bhamani, Amyn Ahmed lyricists_name: Patrick, Tanya lyricists_id: SUDRE45 lyricists_id: SMJAN15 lyricists_id: JJACO76 lyricists_id: DCHEA56 lyricists_id: AABHA42 lyricists_id: TPATR97 actors_name: Jacob, Joseph actors_id: JJACO76 actors_role: owner full_text_status: restricted pres_type: paper series: Lecture Notes in Computer Science, volume 15198 publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) volume: 15198 pagerange: 78-87 event_title: Ethics and Fairness in Medical Imaging Second International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and Third International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with MICCAI 202 issn: 0302-9743 book_title: Ethics and Fairness in Medical Imaging: FAIMI EPIMI 2024 editors_name: Puyol-Antón, Esther editors_name: Zamzmi, Ghada editors_name: Feragen, Aasa editors_name: King, Andrew P editors_name: Cheplygina, Veronika editors_name: Ganz-Benjaminsen, Melanie editors_name: Ferrante, Enzo editors_name: Glocker, Ben editors_name: Petersen, Eike editors_name: Baxter, John SH editors_name: Rekik, Islem editors_name: Eagleson, Roy citation: McCabe, J; Cheng, D; Bhamani, A; Mullin, M; Patrick, T; Nair, A; Janes, SM; ... Jacob, J; + view all <#> McCabe, J; Cheng, D; Bhamani, A; Mullin, M; Patrick, T; Nair, A; Janes, SM; Sudre, CH; Jacob, J; - view fewer <#> (2024) Exploring Fairness in State-of-the-Art Pulmonary Nodule Detection Algorithms. In: Puyol-Antón, Esther and Zamzmi, Ghada and Feragen, Aasa and King, Andrew P and Cheplygina, Veronika and Ganz-Benjaminsen, Melanie and Ferrante, Enzo and Glocker, Ben and Petersen, Eike and Baxter, John SH and Rekik, Islem and Eagleson, Roy, (eds.) Ethics and Fairness in Medical Imaging: FAIMI EPIMI 2024. (pp. pp. 78-87). Springer, Cham document_url: https://discovery.ucl.ac.uk/id/eprint/10200673/1/Fairness_In_SOTA_Nodule_Detection_Algorithms.pdf