Semantic Data Augmentation Enhanced Invariant Risk Minimization for Medical Image Domain Generalization
Journal:
arXiv
Published Date:
Feb 8, 2025
Abstract
Deep learning has achieved remarkable success in medical image
classification. However, its clinical application is often hindered by data
heterogeneity caused by variations in scanner vendors, imaging protocols, and
operators. Approaches such as invariant risk minimization (IRM) aim to address
this challenge of out-of-distribution generalization. For instance, VIRM
improves upon IRM by tackling the issue of insufficient feature support
overlap, demonstrating promising potential. Nonetheless, these methods face
limitations in medical imaging due to the scarcity of annotated data and the
inefficiency of augmentation strategies. To address these issues, we propose a
novel domain-oriented direction selector to replace the random augmentation
strategy used in VIRM. Our method leverages inter-domain covariance as a guider
for augmentation direction, guiding data augmentation towards the target
domain. This approach effectively reduces domain discrepancies and enhances
generalization performance. Experiments on a multi-center diabetic retinopathy
dataset demonstrate that our method outperforms state-of-the-art approaches,
particularly under limited data conditions and significant domain
heterogeneity.
