In this work, we present a novel latent diffusion-based pipeline for 3D kidney anomaly detection on contrast-enhanced abdominal CT. The method combines Denoising Diffusion Probabilistic Models (DDPMs), Denoising Diffusion Implicit Models (DDIMs), and Vector-Quantized Generative Adversarial Networks (VQ-GANs). Unlike prior slice-wise approaches, our method operates directly on an image volume and leverages weak supervision with only case-level pseudo-labels. We benchmark our approach against state-of-the-art supervised segmentation and detection models. This study demonstrates the feasibility and promise of 3D latent diffusion for weakly supervised anomaly detection. While the current results do not yet match supervised baselines, they reveal key directions for improving reconstruction fidelity and lesion localization. Our findings provide an important step toward annotation-efficient, generative modeling of complex abdominal anatomy.

翻译：本研究提出了一种新颖的基于潜在扩散的三维肾脏异常检测流程，适用于对比增强腹部CT影像。该方法融合了去噪扩散概率模型（DDPMs）、去噪扩散隐式模型（DDIMs）以及矢量量化生成对抗网络（VQ-GANs）。与先前基于切片的方法不同，我们的方法直接在图像体数据上运行，并利用仅包含病例级伪标签的弱监督信息。我们将本方法与最先进的监督式分割及检测模型进行了基准测试。本研究证明了三维潜在扩散模型在弱监督异常检测中的可行性与潜力。虽然当前结果尚未达到监督基线的水平，但揭示了提升重建保真度与病灶定位能力的关键方向。我们的研究结果为复杂腹部解剖结构的标注高效生成建模迈出了重要一步。