# An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation

arXiv: https://arxiv.org/abs/2308.13415 · HTML: https://mlsanity.com/paper/2308.13415/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation](https://mlsanity.com/paper/2106.12387/) (2021)
- [Improving the generalizability of convolutional neural network-based segmentation on CMR images](https://mlsanity.com/paper/1907.01268/) (2019)
- [Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection](https://mlsanity.com/paper/2204.01737/) (2022)
- [A systematic study of race and sex bias in CNN-based cardiac MR segmentation](https://mlsanity.com/paper/2209.01627/) (2022)

## Built on by (most cited first, 8 of 8)

- [Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks](https://mlsanity.com/paper/2405.02225/) (2024)
- [Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods](https://mlsanity.com/paper/2510.17999/) (2025)
- [APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation](https://mlsanity.com/paper/2403.05114/) (2024)
- [An investigation into the causes of race bias in AI-based cine CMR segmentation](https://mlsanity.com/paper/2408.02462/) (2024)
- [Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors](https://mlsanity.com/paper/2409.15888/) (2024)
- [Understanding-informed Bias Mitigation for Fair CMR Segmentation](https://mlsanity.com/paper/2503.17089/) (2025)
- [Inspecting Model Fairness in Ultrasound Segmentation Tasks](https://mlsanity.com/paper/2312.02501/) (2023)
- [Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Experts](https://mlsanity.com/paper/2609.38600/) (2026)

## Abstract (arXiv)

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.