Understanding-informed Bias Mitigation for Fair CMR Segmentation

• URL: http://arxiv.org/abs/2503.17089v2
• Date: Thu, 03 Jul 2025 12:11:58 GMT
• Title: Understanding-informed Bias Mitigation for Fair CMR Segmentation
• Authors: Tiarna Lee, Esther Puyol-Antón, Bram Ruijsink, Pier-Giorgio Masci, Louise Keehn, Phil Chowienczyk, Emily Haseler, Miaojing Shi, Andrew P. King,
• Abstract summary: We aim to investigate the impact of common bias mitigation methods to address bias between Black and White subjects in AI-based CMR segmentation models.<n>Specifically, we use oversampling, importance reweighing and Group DRO as well as combinations of these techniques to mitigate the ethnicity bias.<n>We find that bias can be mitigated using oversampling, significantly improving performance for the underrepresented Black subjects whilst not significantly reducing the majority White subjects' performance.
• Score: 7.170614530699774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) is increasingly being used for medical imaging tasks. However, there can be biases in AI models, particularly when they are trained using imbalanced training datasets. One such example has been the strong ethnicity bias effect in cardiac magnetic resonance (CMR) image segmentation models. Although this phenomenon has been reported in a number of publications, little is known about the effectiveness of bias mitigation algorithms in this domain. We aim to investigate the impact of common bias mitigation methods to address bias between Black and White subjects in AI-based CMR segmentation models. Specifically, we use oversampling, importance reweighing and Group DRO as well as combinations of these techniques to mitigate the ethnicity bias. Second, motivated by recent findings on the root causes of AI-based CMR segmentation bias, we evaluate the same methods using models trained and evaluated on cropped CMR images. We find that bias can be mitigated using oversampling, significantly improving performance for the underrepresented Black subjects whilst not significantly reducing the majority White subjects' performance. Using cropped images increases performance for both ethnicities and reduces the bias, whilst adding oversampling as a bias mitigation technique with cropped images reduces the bias further. When testing the models on an external clinical validation set, we find high segmentation performance and no statistically significant bias.

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