研究用不确定性评估扩散模型生成医学图像的解剖正确性
Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis
医学影像合成的怕生成得像但解剖错了，这篇用 AortaDiff 加 6 种不确定性方法来量化这事，MCDropout 免训练就能用
一项 arXiv 研究探索用不确定性作为扩散模型合成医学图像的语义正确性代理指标。研究基于 AortaDiff 框架，将非增强 CT（NCCT）合成为增强 CT（CECT），并同时生成血管腔分割以量化生成结果的解剖正确性。作者比较了 Ensemble、HyperDiff、BayesDiff、MCDropout、RDS、TTA 共 6 种不确定性方法，在像素、区域和图像三个层级评估。结果显示 MCDropout 表现最均衡，可直接用于已用 dropout 训练的模型且无需额外训练，还能支持分布外（OOD）病例检测。
Uncertainty as a Proxy for Semantic Correctness in Diffusion-Based Medical Image Synthesis
Diffusion models can synthesise contrast-enhanced CT (CECT) from non-contrast CT (NCCT), avoiding contrast administration and its environmental and patient-access costs. However, visually realistic images are not necessarily anatomically correct, and the pixel-intensity and feature-space similarity metrics used to assess generation quality do not directly measure anatomical correctness. In this work, we investigate whether uncertainty can serve as a proxy for semantic correctness in diffusion-based medical image synthesis. We study NCCT-to-CECT synthesis using AortaDiff, a multitask diffusion framework that jointly generates CECT images and lumen segmentations. The segmentation output provides an explicit representation of the generated vascular anatomy, enabling segmentation-derived errors to be used as a quantitative measure of generation correctness. Six methods spanning weight (Ensemble, HyperDiff, BayesDiff), architecture-perturbation (MCDropout), generative-stochasticity (RDS) and input-perturbation (TTA) uncertainty are compared at the pixel, region and image levels, and for detection of clinically relevant out-of-distribution (OOD) cases. Uncertainty proves informative at all three spatial scales, remains informative on an external multi-centre dataset under distribution shift, and supports OOD detection. MCDropout stands out among the six: it ranks among the leading methods at every scale, generalizes well on the external dataset, and can be enabled at inference on any model already trained with dropout, so reliable uncertainty comes at no extra training cost. Uncertainty reliably flags severe failures but discriminates poorly among already high-quality images. These findings support uncertainty as a practical and computationally economical signal for quality filtering, reliability assessment and OOD detection in NCCT-to CECT synthesis.
