Abstract
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.
Abstract (translated)
在这项工作中，我们提出了一种新颖的基于潜在扩散的管道，用于在增强腹部CT图像上进行3D肾脏异常检测。该方法结合了去噪扩散概率模型（DDPMs）、去噪扩散隐式模型（DDIMs）和向量量化生成对抗网络（VQ-GANs）。与之前的逐片处理方法不同，我们的方法直接针对整个图像体积进行操作，并利用仅基于病例级别的伪标签的弱监督。我们将该方法与最先进的有监督分割和检测模型进行了基准测试。这项研究表明了在弱监督下使用3D潜在扩散技术进行异常检测的可能性和前景。尽管目前的结果尚未达到有监督基线的标准，但它们揭示了提高重建准确性和病灶定位的关键方向。我们的研究为高效注释、复杂腹部解剖结构的生成建模提供了一个重要的步骤。
URL
https://arxiv.org/abs/2601.05852
