MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis

2026-09-15

AI Summary

本文提出MUMINS，一种基于元数据条件和不确定性感知的医学图像未来状态合成方法，有效预测解剖学变化并量化不确定性。

Abstract

Forecasting anatomical changes such as tumor growth and neurodegeneration is a challenging generative vision task. Morphological evolution is subtle relative to static anatomy, highly patient-specific, and inherently stochastic. Existing methods struggle with several issues: deterministic networks ignore biological stochasticity, while standard diffusion models require computationally prohibitive multi-pass sampling to quantify uncertainty. We propose MUMINS (Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis), an efficient diffusion framework that jointly diffuses a baseline scan and its follow-up residual, summed to synthesize the follow-up scan, while concurrently predicting a spatial uncertainty map, in a single reverse diffusion process. Conditioned on the time interval and relevant metadata, it preserves fine-grained anatomy by dynamically re-injecting the baseline as a soft anchor at every denoising step, and a negative-log-likelihood head learns the uncertainty map to explicitly flag error-prone regions. Designed without organ-specific heuristics, the same architecture is reused across anatomies via separate, dataset-specific retraining. Extensive evaluations demonstrate that dataset-specific retraining of MUMINS matches or outperforms dedicated, domain-specific state-of-the-art methods on lung CT (PNG) and brain MRI (OASIS-3). Project page: https://github.com/aolivtous/MUMINS.

Problem

• anatomical changes
• uncertainty quantification
• medical image synthesis
• stochasticity
• diffusion models

Innovation

• Metadata-conditioned
• Uncertainty-aware
• Single reverse diffusion process
• Spatial uncertainty map
• Dataset-specific retraining