Denoising for Relaxing: Unsupervised Domain Adaptive Fundus Image Segmentation Without Source Data

Abstract

Recently, unsupervised domain adaptation (UDA) has been actively explored for multi-site fundus image segmentation with domain discrepancy. Despite relaxing the requirement of target labels, typical UDA still requires the labeled source data to achieve distribution alignment during adaptation. Unfortunately, due to privacy concerns, the vendor side often cannot provide the source data to the targeted client side in clinical practice, making the adaptation more challenging. To address this, in this work, we present a novel uncertainty-rectified denoising-for-relaxing (U-D4R) framework, aiming at completely relaxing the source data and effectively adapting the pretrained source model to the target domain. Considering the unreliable source model predictions on the target domain, we first present an adaptive class-dependent threshold strategy as the coarse denoising process to generate the pseudo labels. Then, the uncertainty-rectified label soft correction is introduced for fine denoising by taking advantage of estimating the joint distribution matrix between the observed and latent labels. Extensive experiments on cross-domain fundus image segmentation showed that our approach significantly outperforms the state-of-the-art source-free methods and encouragingly achieves comparable or even better performances over the leading source-dependent methods.