Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling

• URL: http://arxiv.org/abs/2109.09735v1
• Date: Sun, 19 Sep 2021 06:38:21 GMT
• Title: Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
• Authors: Cheng Chen, Quande Liu, Yueming Jin, Qi Dou, Pheng-Ann Heng
• Abstract summary: Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. In many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data.
• Score: 56.98020855107174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. This paper studies the practical yet challenging source-free unsupervised domain adaptation problem, in which only an existing source model and the unlabeled target data are available for model adaptation. We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels. Importantly, considering that the pseudo labels generated from source model are inevitably noisy due to domain shift, we further introduce two complementary pixel-level and class-level denoising schemes with uncertainty estimation and prototype estimation to reduce noisy pseudo labels and select reliable ones to enhance the pseudo-labeling efficacy. Experimental results on cross-domain fundus image segmentation show that without using any source images or altering source training, our approach achieves comparable or even higher performance than state-of-the-art source-dependent unsupervised domain adaptation methods.

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