Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence
Motivation: Diffusion model (DM)-based methods demonstrate competitive performance in solving medical imaging inverse problems. However, it remains uncertain whether these methods are vulnerable to distribution shifts, a common issue in traditional learning-based approaches.
Goal(s): Our goal is to conduct experiments to evaluate how well DM-based methods handle distribution shifts in various scenarios.
Approach: We utilize three different sampling methods based on a pretrained Diffusion model and apply them to four clinical tasks without fine-tuning.
Results: Experiments indicate that DM-based methods efficiently address distribution shifts without requiring fine-tuning. They exhibit robustness to many distribution shifts, even when the test data deviates from the training data.
Impact: DM-based methods reduce resource consumption and the need for extensive training datasets, potentially inspiring further development of DM-based techniques for enhancing in-vivo tasks with limited resources.
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