Unsupervised Search for Ethnic Minorities' Medical Segmentation Training Set
Journal:
arXiv
Published Date:
Jan 5, 2025
Abstract
This article investigates the critical issue of dataset bias in medical
imaging, with a particular emphasis on racial disparities caused by uneven
population distribution in dataset collection. Our analysis reveals that
medical segmentation datasets are significantly biased, primarily influenced by
the demographic composition of their collection sites. For instance, Scanning
Laser Ophthalmoscopy (SLO) fundus datasets collected in the United States
predominantly feature images of White individuals, with minority racial groups
underrepresented. This imbalance can result in biased model performance and
inequitable clinical outcomes, particularly for minority populations. To
address this challenge, we propose a novel training set search strategy aimed
at reducing these biases by focusing on underrepresented racial groups. Our
approach utilizes existing datasets and employs a simple greedy algorithm to
identify source images that closely match the target domain distribution. By
selecting training data that aligns more closely with the characteristics of
minority populations, our strategy improves the accuracy of medical
segmentation models on specific minorities, i.e., Black. Our experimental
results demonstrate the effectiveness of this approach in mitigating bias. We
also discuss the broader societal implications, highlighting how addressing
these disparities can contribute to more equitable healthcare outcomes.
