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

by   Cheng Chen, et al.

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.


page 7

page 8


Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation

Conventional unsupervised domain adaptation (UDA) methods need to access...

Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation

In the domain adaptation problem, source data may be unavailable to the ...

Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision

Unsupervised Domain Adaptation (UDA) has attracted a surge of interest o...

On the Benefits of Selectivity in Pseudo-Labeling for Unsupervised Multi-Source-Free Domain Adaptation

Due to privacy, storage, and other constraints, there is a growing need ...

C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation

Unsupervised domain adaptation (UDA) approaches focus on adapting models...

Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples

Deploying deep visual models can lead to performance drops due to the di...

GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Dense retrieval approaches can overcome the lexical gap and lead to sign...

Please sign up or login with your details

Forgot password? Click here to reset