AdverIN: Monotonic Adversarial Intensity Attack for Domain Generalization in Medical Image Segmentation
Domain generalization (DG) has been shown as a promising research direction since it can potentially enable deep learning models to handle data from previously unseen domains. DG methods try to achieve this by learning domain-invariant features that are robust to variations across different domains. This work proposes a novel domain generalization (DG) technique termed Adversarial Intensity Attack (AdverIN). AdverIN leverages an adversarial training strategy to augment data diversity by synthesizing a spectrum of intensity variations while preserving essential contextual information within the images. To evaluate the efficacy of AdverIN, we conducted rigorous experiments on diverse multi-domain medical image segmentation tasks. This included 2D retinal optic disc/cup segmentation and 3D prostate MRI segmentation. Our results demonstrate that AdverIN significantly improves the generalizability of segmentation models, achieving state-of-the-art performance on these challenging datasets.