arXiv Computer Vision September 15, 2026 By Yan Zeng, Changlu Guo, Anders Nymark Christensen, Morten Rieger Hannemose, Anders Bjorholm Dahl

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

Read the original on arXiv Computer Vision →

MedDiME is a latent-space, classifier‑guided diffusion framework designed for medical counterfactual image generation. It introduces a gradient‑driven adaptive masking mechanism that works directly in latent space, enabling spatially precise edits while avoiding the high computational and memory costs of pixel‑space methods. Experiments show MedDiME can produce high‑quality counterfactuals up to 40× faster and using 13× less GPU memory than previous diffusion baselines.

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