Bias and Generalizability of Foundation Models across Datasets in Breast Mammography
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
Over the past decades, computer-aided diagnosis tools for breast cancer have
been developed to enhance screening procedures, yet their clinical adoption
remains challenged by data variability and inherent biases. Although foundation
models (FMs) have recently demonstrated impressive generalizability and
transfer learning capabilities by leveraging vast and diverse datasets, their
performance can be undermined by spurious correlations that arise from
variations in image quality, labeling uncertainty, and sensitive patient
attributes. In this work, we explore the fairness and bias of FMs for breast
mammography classification by leveraging a large pool of datasets from diverse
sources-including data from underrepresented regions and an in-house dataset.
Our extensive experiments show that while modality-specific pre-training of FMs
enhances performance, classifiers trained on features from individual datasets
fail to generalize across domains. Aggregating datasets improves overall
performance, yet does not fully mitigate biases, leading to significant
disparities across under-represented subgroups such as extreme breast densities
and age groups. Furthermore, while domain-adaptation strategies can reduce
these disparities, they often incur a performance trade-off. In contrast,
fairness-aware techniques yield more stable and equitable performance across
subgroups. These findings underscore the necessity of incorporating rigorous
fairness evaluations and mitigation strategies into FM-based models to foster
inclusive and generalizable AI.
