mlsanityData to 14 Oct 2026

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

arXiv 2308.13415 ↗First posted 25 Aug 2023eess.IV cs.CV cs.LGcited by 8

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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.

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.

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9 of 17 references.

References · 9

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  4. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,222
  5. 2016The Importance of Skip Connections in Biomedical Image Segmentationcited by 126
  6. 2021Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentationcited by 57
  7. 2019Improving the generalizability of convolutional neural network-based segmentation on CMR imagescited by 25
  8. 2022Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detectioncited by 19
  9. 2022A systematic study of race and sex bias in CNN-based cardiac MR segmentationcited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 2; 2026: 1.

8 papers.

  1. 2024Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Riskscited by 14
  2. 2025Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methodscited by 3
  3. 2024APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigationcited by 2
  4. 2024An investigation into the causes of race bias in AI-based cine CMR segmentationcited by 1
  5. 2024Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priorscited by 1
  6. 2025Understanding-informed Bias Mitigation for Fair CMR Segmentationcited by 1
  7. 2023Inspecting Model Fairness in Ultrasound Segmentation Taskscited by 0
  8. 2026Sense and Sensitivity: Benchmarking LLM Clinical Triage Recommendations with Physician Expertscited by 0