Inspecting Model Fairness in Ultrasound Segmentation Tasks
Zikang Xu Fenghe Tang Quan Quan Jianrui Ding Chunping Ning S. Kevin Zhou
With the rapid expansion of machine learning and deep learning (DL),
researchers are increasingly employing learning-based algorithms to alleviate
diagnostic challenges across diverse medical tasks and applications. While
advancements in diagnostic precision are notable, some researchers have
identified a concerning trend: their models exhibit biased performance across
subgroups characterized by different sensitive attributes. This bias not only
infringes upon the rights of patients but also has the potential to lead to
life-altering consequences. In this paper, we inspect a series of DL
segmentation models using two ultrasound datasets, aiming to assess the
presence of model unfairness in these specific tasks. Our findings reveal that
even state-of-the-art DL algorithms demonstrate unfair behavior in ultrasound
segmentation tasks. These results serve as a crucial warning, underscoring the
necessity for careful model evaluation before their deployment in real-world
scenarios. Such assessments are imperative to ensure ethical considerations and
mitigate the risk of adverse impacts on patient outcomes.