Supportive Negatives Spectral Augmentation for Source-Free Cross-Domain Segmentation

Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation tasks due to its data-privacy protection and achieve promising performances. However, cross-domain distribution shift makes it difficult for the adapted model to provide accurate decisions on several hard instances and negatively affects model generalization. To overcome this limitation, a novel method 'supportive negatives spectral augmentation' (SNSA) is presented in this work. Concretely, SNSA includes the instance selection mechanism to automatically discover a few hard samples for which the source model produces incorrect predictions. And an active learning strategy is adopted to re-calibrate their predictive masks. Moreover, SNSA deploys the spectral augmentation between hard instances and others to encourage the source model to gradually capture and adapt the attributions of the target distribution. Considerable experimental studies demonstrate that annotating merely 4%∼5% of negative instances from the target domain significantly improves the segmentation performance over previous methods.