AI 中文总结
研究针对弱监督医学成像系统各阶段临床信息保留评估有限的问题，提出诊断差距框架，通过对特定病变ROI作物评估三个重建器，依重建保真度衡量决策与解释保留，能识别多阶段管道何时何地丢失诊断信号。
AI 中文摘要
近年来，用于医学成像的弱监督管道越来越受欢迎。这些系统通常包括多个阶段和组件，如重建、生成和定位，但标准评估指标对各阶段是否保留临床相关信息的洞察有限。我们提出了诊断差距框架，这是一种实用的评估工具，可根据测量的重建保真度来衡量决策保留和解释保留。为了分离重建与定位的影响，我们使用由三个类条件重建器（VQ-VAE-GAN、VAE-GAN和扩散（SDEdit））组成的保真度阶梯，在感知距离（LPIPS 0.029 - 0.584）的二十倍范围内，对精心策划的病变ROI作物进行评估。在自动编码器保真度下，决策和解释都得以保留：AUC变化保持在±0.005以内，归因相似度（HiResCAM、Grad-CAM++）保持较高。在扩散保真度下，两者都崩溃：汇总AUC下降0.253，肿块-病理学AUC降至随机水平以下。因此，诊断差距是重建保真度的可测量函数，而不是重建的固有成本，该框架提供了一种与架构无关的工具，用于识别多阶段管道何时何地失去诊断信号。
英文摘要
Weakly supervised pipelines for medical imaging have become increasingly popular over the years. These systems often include multiple stages and components, such as reconstruction, generation, and localization, yet standard evaluation metrics provide limited insight into whether clinically relevant information is preserved across each stage. We present the diagnostic gap framework, a practical evaluation tool that measures decision preservation and explanation preservation as a function of measured reconstruction fidelity. To isolate the effect of reconstruction from localization, we evaluate on curated lesion ROI crops using a fidelity ladder of three class-conditional reconstructors---VQ-VAE-GAN, VAE-GAN, and diffusion (SDEdit)---spanning a twenty-fold range in perceptual distance (LPIPS 0.029--0.584). At autoencoder fidelity, both decision and explanation are preserved: AUC changes remain within $\pm$0.005 and attribution similarity (HiResCAM, Grad-CAM++) stays high. At diffusion fidelity, both collapse: pooled AUC drops by 0.253 and mass-pathology AUC falls below chance. The diagnostic gap is thus a measurable function of reconstruction fidelity rather than an intrinsic cost of reconstruction, and the framework provides an architecture-agnostic instrument for identifying when and where multi-stage pipelines lose diagnostic signal.
