Adaptive Pseudo Labeling for Source-Free Domain Adaptation in Medical Image Segmentation

Abstract

Domain adaptation is common but challenging in signal processing tasks due to the intrinsic discrepancy, especially in difficult-to-label medical image segmentation application scenarios. Pseudo labeling methods are widely utilized to compensate for the scarcity of annotation. However, most existing methods set the fixed thresholds to select highly-confident predictions as pseudo labels, inevitably generating false labels with noise. In this paper, we combine the dual-classifiers consistency and predictive category-aware confidence to form a novel regularization for pseudo-label denoising. The dual-classifiers consistency helps promote the robustness of pseudo labels. Meanwhile, category-aware confidence is utilized as adaptive pixel-wise weights, avoiding the need for handcrafted thresholds. The adapted model is refined by the rectified pseudo labels without source domain samples. The proposed method is model-independent and thus can be plug-and-play to improve existing UDA methods. We validated it on the cross-modality medical image segmentation and obtained more competitive results.