Quest for Clone: Test-Time Domain Adaptation for Medical Image Segmentation by Searching the Closest Clone in Latent Space

Unsupervised Domain Adaptation (UDA) aims to align labeled source distribution and unlabeled target distribution by mining domain-agnostic feature representation. However, adapting the source-trained model for new target domains after the model is deployed to users poses a significant challenge. To address this, we propose a generative latent search paradigm to reconstruct the closest clone of every target image from the source latent space. This involves utilizing a test-time adaptation (TTA) strategy, wherein a latent optimization step finds the closest clone of each target image from the source representation space using variational sampling of source latent distribution. Thus, our method facilitates domain adaptation without requiring target-domain supervision during training. Moreover, we demonstrate that our approach can be further fine-tuned using a few labeled target data without the need for unlabeled target data, by leveraging global and local label guidance from available target annotations to enhance the downstream segmentation task. We empirically validate the efficacy of our proposed method, surpassing existing UDA, TTA, and SSDA methods in two domain adaptive image segmentation tasks. Code is available at: GitHub.