# A systematic study of race and sex bias in CNN-based cardiac MR segmentation

arXiv: https://arxiv.org/abs/2209.01627 · HTML: https://mlsanity.com/paper/2209.01627/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 4 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Reading Race: AI Recognises Patient's Racial Identity In Medical Images](https://mlsanity.com/paper/2107.10356/) (2021)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [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)
- [Risk of Training Diagnostic Algorithms on Data with Demographic Bias](https://mlsanity.com/paper/2005.10050/) (2020)
- [Not Color Blind: AI Predicts Racial Identity from Black and White Retinal Vessel Segmentations](https://mlsanity.com/paper/2109.13845/) (2021)

## Built on by (most cited first, 9 of 9)

- [An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation](https://mlsanity.com/paper/2308.13415/) (2023)
- [Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation](https://mlsanity.com/paper/2408.04610/) (2024)
- [An investigation into the causes of race bias in AI-based cine CMR segmentation](https://mlsanity.com/paper/2408.02462/) (2024)
- [Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms](https://mlsanity.com/paper/2409.19371/) (2024)
- [Understanding-informed Bias Mitigation for Fair CMR Segmentation](https://mlsanity.com/paper/2503.17089/) (2025)
- [A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes](https://mlsanity.com/paper/2308.12675/) (2023)
- [An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features](https://mlsanity.com/paper/2309.17197/) (2023)
- [Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model Independent Analysis for Multiple Anatomical Structures](https://mlsanity.com/paper/2509.19778/) (2025)
- [Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance](https://mlsanity.com/paper/2604.10904/) (2026)

## Abstract (arXiv)

In computer vision there has been significant research interest in assessing potential demographic bias in deep learning models. One of the main causes of such bias is imbalance in the training data. In medical imaging, where the potential impact of bias is arguably much greater, there has been less interest. In medical imaging pipelines, segmentation of structures of interest plays an important role in estimating clinical biomarkers that are subsequently used to inform patient management. Convolutional neural networks (CNNs) are starting to be used to automate this process. We present the first systematic study of the impact of training set imbalance on race and sex bias in CNN-based segmentation. We focus on segmentation of the structures of the heart from short axis cine cardiac magnetic resonance images, and train multiple CNN segmentation models with different levels of race/sex imbalance. We find no significant bias in the sex experiment but significant bias in two separate race experiments, highlighting the need to consider adequate representation of different demographic groups in health datasets.