MedDiME: Efficient Latent Diffusion with Adaptive Masking for Medical Counterfactual Generation

2026-09-14

AI Summary

为解决医学反事实生成中计算慢和内存消耗大的问题，提出MedDiME框架，采用潜在空间分类器引导扩散和自适应掩码机制，提高生成效率。

Abstract

Medical counterfactual generation modifies images to change model predictions for interpretability. However, existing diffusion-based approaches are often prohibitively slow and memory-intensive, making them difficult to apply in high-resolution settings. Moreover, existing masking strategies are tightly coupled with pixel-space representations, making them incompatible with latent-space diffusion editing. To address these challenges, we propose MedDiME, a latent-space classifier-guided diffusion framework that reduces computational and memory overhead while introducing a latent-compatible, gradient-driven adaptive masking mechanism for spatially precise medical counterfactual generation. Extensive experiments demonstrate that MedDiME achieves high-quality counterfactual generation with significant efficiency gains compared to prior classifier-guided diffusion baselines, achieving up to 40 times faster inference and 13 times lower peak GPU memory usage.

Problem

• Medical Counterfactual Generation
• Diffusion-based Approaches
• Latent Space
• Adaptive Masking
• Computational Efficiency

Innovation

• latent-space diffusion
• adaptive masking
• gradient-driven
• medical counterfactual generation
• efficiency improvement