Uncertainty-Aware Data Augmentation for Robust Medical Image Segmentation

Medical image segmentation plays a crucial role in various clinical applications, including diagnosis, treatment planning, and surgical guidance. However, the inherent variability in medical images, coupled with limited annotated data, often leads to deep learning models that lack robustness and generalize poorly to unseen data. This paper introduces an uncertainty-aware data augmentation framework designed to enhance the robustness of medical image segmentation models. Our approach dynamically generates augmented training samples by considering the model's uncertainty in segmentation predictions. Specifically, we leverage Monte Carlo Dropout to estimate pixel-wise uncertainty and prioritize augmentation strategies that target regions of high uncertainty. This adaptive augmentation scheme encourages the model to learn more robust features in challenging or ambiguous areas, thereby improving its generalization capabilities. Extensive experiments on multiple public medical image segmentation benchmarks demonstrate that our uncertainty-aware data augmentation significantly improves the robustness and performance of state-of-the-art segmentation networks, particularly in scenarios with limited training data.