Toward Fair Ultrasound Computing Tomography: Challenges, Solutions and Outlook

Yi Sheng, Junhuan Yang, Youzuo Lin, Weiwen Jiang, Lei Yang

Abstract: Medical image reconstruction plays a pivotal role in early cancer detection, which can significantly enhance both the quality and longevity of a patient’s life through timely treatment. However, the extent to which current image reconstruction methods accurately represent all populations, and whether they underperform for certain groups, remains largely unexplored. In this work, we will examine the deep learning (DL)–based approach to image reconstruction and its associated fairness concerns. Initially, our experiments confirmed the unfairness’s presence. Subsequently, by addressing the issue from two perspectives, we gained valuable insights, which deepened our understanding of the problem. To assess a model’s fairness, it’s crucial to evaluate it from various perspectives, as relying on a single metric can often yield misleading results.