用3D扩散模型实现弱监督肾癌检测，省去逐层标注
Kidney Cancer Detection Using 3D-Based Latent Diffusion Models
- 基于3D潜空间扩散模型，直接处理腹部CT体数据
- 仅需病例级伪标签，无需逐切片标注，表现接近有监督方法
- 为减少标注成本的医学图像异常检测提供新思路
本文提出一种基于潜空间扩散模型的3D肾部异常检测新框架，用于增强型腹部CT影像。该方法融合去噪扩散概率模型（DDPM）、去噪扩散隐式模型（DDIM）与向量量化生成对抗网络（VQ-GAN），突破以往逐切片分析的局限，直接在体数据上操作，并采用仅需病例级伪标签的弱监督策略。我们在多个主流基准上对比了当前最先进的有监督分割与检测模型。结果表明，3D潜空间扩散模型在弱监督条件下具备可行性与潜力。尽管当前性能尚未达到有监督基线水平，但揭示了提升重建保真度与病灶定位精度的关键方向。本研究为复杂腹腔解剖结构的低标注成本生成建模迈出重要一步。
原文摘要 · Abstract (English)
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.
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