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