arXiv cs.AI· Anna Oliveras, Roger Mar\'i, Rafael Redondo, Oriol Guardi\`a, Cynthia Ifeyinwa Ugwu, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, Petia Radeva·· 4 小时前AI 评分37
MUMINS：元数据条件、不确定性感知的医学影像后续状态合成
MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis
AI 导读
MUMINS 是一种高效扩散框架，在单次反向扩散中联合扩散基线扫描与随访残差以合成随访影像，同时生成空间不确定性图，以标注易出错区域。该框架以时间间隔和相关元数据为条件，并在每步去噪中动态重注入基线作为软锚点以保留细粒度解剖结构。同一架构经数据集专门重训练后，在肺 CT（PNG）和脑 MRI（OASIS-3）上匹配或超越领域专用 SOTA 方法。
正文 · 原文
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: this https URL.
| Comments: | Supplementary material to follow in future versions |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| ACM classes: | I.2.6; I.4.5; I.4.9; J.3 |
| Cite as: | arXiv:2609.17169 [cs.CV] |
| (or arXiv:2609.17169v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2609.17169 arXiv-issued DOI via DataCite |
Submission history
From: Anna Oliveras [view email]
[v1]
Tue, 15 Sep 2026 13:32:37 UTC (1,538 KB)
[v2]
Tue, 27 Oct 2026 11:31:01 UTC (1,464 KB)
来源：arXiv cs.AI · arxiv.org