Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration
This addresses generalization issues in medical image segmentation across different data acquisition domains, representing an incremental improvement over existing methods.
The paper tackles the problem of medical image segmentation models failing to generalize to unseen domains due to domain shift, by proposing a multi-task approach with domain-specific image restoration and random amplitude mixup that achieves state-of-the-art performance on two public benchmarks.
For medical image analysis, segmentation models trained on one or several domains lack generalization ability to unseen domains due to discrepancies between different data acquisition policies. We argue that the degeneration in segmentation performance is mainly attributed to overfitting to source domains and domain shift. To this end, we present a novel generalizable medical image segmentation method. To be specific, we design our approach as a multi-task paradigm by combining the segmentation model with a self-supervision domain-specific image restoration (DSIR) module for model regularization. We also design a random amplitude mixup (RAM) module, which incorporates low-level frequency information of different domain images to synthesize new images. To guide our model be resistant to domain shift, we introduce a semantic consistency loss. We demonstrate the performance of our method on two public generalizable segmentation benchmarks in medical images, which validates our method could achieve the state-of-the-art performance.