HEAL: Learning-Free Source Free Unsupervised Domain Adaptation for Cross-Modality Medical Image Segmentation

TL;DR

HEAL is a novel source-free unsupervised domain adaptation framework for cross-modality medical image segmentation that leverages hierarchical denoising, edge-guided selection, and size-aware fusion to outperform existing methods.

Contribution

HEAL introduces a learning-free, source-free framework with innovative modules for effective cross-modality medical image segmentation without source data or labels.

Findings

Outperforms existing SFUDA methods in cross-modality segmentation tasks.

Achieves state-of-the-art performance on large-scale experiments.

Demonstrates robustness without source data or target labels.

Abstract

Growing demands for clinical data privacy and storage constraints have spurred advances in Source Free Unsupervised Domain Adaptation (SFUDA). SFUDA addresses the domain shift by adapting models from the source domain to the unseen target domain without accessing source data, even when target-domain labels are unavailable. However, SFUDA faces significant challenges: the absence of source domain data and label supervision in the target domain due to source free and unsupervised settings. To address these issues, we propose HEAL, a novel SFUDA framework that integrates Hierarchical denoising, Edge-guided selection, size-Aware fusion, and Learning-free characteristic. Large-scale cross-modality experiments demonstrate that our method outperforms existing SFUDA approaches, achieving state-of-the-art (SOTA) performance. The source code is publicly available at: https://github.com/derekshiii/HEAL.