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

TL;DR

This paper introduces RPANet, a source-free domain adaptation framework for polyp segmentation that employs coarse-to-fine self-supervision to improve target domain performance without source data.

Contribution

The paper proposes a novel SFDA framework with region-to-pixel adaptation using contrastive learning and pseudo-label refinement, addressing key challenges in unsupervised medical image segmentation.

Findings

RPANet outperforms state-of-the-art SFDA and UDA methods.

Effective region-level and pixel-level discriminative representations.

Robustness to noisy pseudo labels and overconfidence issues.

Abstract

Unsupervised Domain Adaptation~(UDA) has attracted a surge of interest over the past decade but is difficult to be used in real-world applications. Considering the privacy-preservation issues and security concerns, in this work, we study a practical problem of Source-Free Domain Adaptation (SFDA), which eliminates the reliance on annotated source data. Current SFDA methods focus on extracting domain knowledge from the source-trained model but neglects the intrinsic structure of the target domain. Moreover, they typically utilize pseudo labels for self-training in the target domain, but suffer from the notorious error accumulation problem. To address these issues, we propose a new SFDA framework, called Region-to-Pixel Adaptation Network~(RPANet), which learns the region-level and pixel-level discriminative representations through coarse-to-fine self-supervision. The proposed RPANet consists of two modules, Foreground-aware Contrastive Learning (FCL) and Confidence-Calibrated Pseudo-Labeling (CCPL), which explicitly address the key challenges of ``how to distinguish'' and ``how to refine''. To be specific, FCL introduces a supervised contrastive learning paradigm in the region level to contrast different region centroids across different target images, which efficiently involves all pseudo labels while robust to noisy samples. CCPL designs a novel fusion strategy to reduce the overconfidence problem of pseudo labels by fusing two different target predictions without introducing any additional network modules. Extensive experiments on three cross-domain polyp segmentation tasks reveal that RPANet significantly outperforms state-of-the-art SFDA and UDA methods without access to source data, revealing the potential of SFDA in medical applications.