Abstract
The purpose of this study is to improve Unsupervised Domain Adaptation (UDA) by utilizing intermediate image distributions from the source domain to the target-like domain during the image generation process. However, image generators like Generative Adversarial Networks (GANs) can be regarded as black boxes due to their complex internal workings, and we can only access the final generated image. This limitation makes them unable for UDA to use the available knowledge of the intermediate distribution produced in the generation process when executing domain alignment. To address this problem, we propose a novel UDA framework that utilizes diffusion models to capture and transfer an amount of inter-domain knowledge, thereby mitigating the domain shift problem. A coupled structure-preserved diffusion model is designed to synthesize intermediate images in multiple steps, making the intermediate image distributions accessible. A stochastic step alignment strategy is further developed to align feature distributions, resulting in improved adaptation ability. The effectiveness of the proposed method is demonstrated through experiments on abdominal multi-organ segmentation.
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This study was funded by the Hong Kong Research Grants Council under Grant 16214521.
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Ji, W., Chung, A.C.S. (2024). Diffusion-Based Domain Adaptation for Medical Image Segmentation Using Stochastic Step Alignment. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15008. Springer, Cham. https://doi.org/10.1007/978-3-031-72111-3_18
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