ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Journal:
arXiv
Published Date:
Jun 12, 2025
Abstract
Medical images are usually collected from multiple domains, leading to domain
shifts that impair the performance of medical image segmentation models. Domain
Generalization (DG) aims to address this issue by training a robust model with
strong generalizability. Recently, numerous domain randomization-based DG
methods have been proposed. However, these methods suffer from the following
limitations: 1) constrained efficiency of domain randomization due to their
exclusive dependence on image style perturbation, and 2) neglect of the adverse
effects of over-augmented images on model training. To address these issues, we
propose a novel domain randomization-based DG method, called content style
augmentation (ConStyX), for generalizable medical image segmentation.
Specifically, ConStyX 1) augments the content and style of training data,
allowing the augmented training data to better cover a wider range of data
domains, and 2) leverages well-augmented features while mitigating the negative
effects of over-augmented features during model training. Extensive experiments
across multiple domains demonstrate that our ConStyX achieves superior
generalization performance. The code is available at
https://github.com/jwxsp1/ConStyX.
