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This study confirms the pivotal role of anatomical priors, offering a novel paradigm for balancing attack imperceptibility with efficiency and pro viding a low-resource framework for evaluating medical AI robustness.
A Prior-guided Uncertainty-aware Residual Diffusion model (PURD), designed to generate accurate and anatomically consistent multi-modal MR images, and adopts a residual diffusion framework-replacing traditional Gaussian diffusion-to more effectively capture subtle lesion details.
A labeled patch guidance (LPG) for patch-level interactive enhancement module that optimizes pseudo-label quality for unlabeled data by dynamically mining anatomical prior knowledge from labeled data is proposed.
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