An Investigation into the Impact of Deep Learning Model Choice on Sex and Race Bias in Cardiac MR Segmentation

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)


In medical imaging, artificial intelligence (AI) is increasingly being used to automate routine tasks. However, these algorithms can exhibit and exacerbate biases which lead to disparate performances between protected groups. We investigate the impact of model choice on how imbalances in subject sex and race in training datasets affect AI-based cine cardiac magnetic resonance image segmentation. We evaluate three convolutional neural network-based models and one vision transformer model. We find significant sex bias in three of the four models and racial bias in all of the models. However, the severity and nature of the bias varies between the models, highlighting the importance of model choice when attempting to train fair AI-based segmentation models for medical imaging tasks.

Original languageEnglish
Title of host publicationClinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging - 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, EPIMI 2023, Proceedings
EditorsStefan Wesarg, Cristina Oyarzun Laura, Esther Puyol Antón, Andrew P. King, John S.H. Baxter, Marius Erdt, Klaus Drechsler, Moti Freiman, Yufei Chen, Islem Rekik, Roy Eagleson, Aasa Feragen, Veronika Cheplygina, Melani Ganz-Benjaminsen, Enzo Ferrante, Ben Glocker, Daniel Moyer, Eikel Petersen
Number of pages10
Publication statusPublished - 9 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14242 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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