Source-Free Domain Adaptation for Image Segmentation

Mathilde Bateson, Hoel Kervadec, Jose Dolz, Herve Lombaert, Ismail Ben Ayed

Keywords: source-free domain adaptation, segmentation

TL;DR: We tackle source-free domain adaptation (SFDA) for semantic segmentation.

Abstract:

Domain adaptation (DA) tackles the performance drop observed when applying a model on target data from a different domain than the training one. However, most common DA techniques require concurrent access to the input images of both the source and target domains, which is often impossible for privacy concerns. We introduce a source-free domain adaptation for image segmentation, leveraging a prior-aware entropy minimization. We validate on spine, prostate and cardiac segmentation problems.
Our method yields comparable results to several state-of-the-art adaptation techniques, despite having access to much less information. Our framework can be used in many segmentation problems, and our code is publicly available at \url{https://github.com/mathilde-b/SFDA}

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Paper Type: recently published or submitted journal contributions

Primary Subject Area: Transfer Learning and Domain Adaptation

Secondary Subject Area: Segmentation

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