Imperial College London（帝国理工学院）

A Unifying Perspective on Causal World Models: From Observations to Representations to Structure

因果世界模型的统一视角：从观测到表征再到结构

Avinash Kori, Fabrizio Russo

机构 * Imperial College London（帝国理工学院） 纠错

AI总结 本文从因果视角研究不同抽象层级的世界模型，提出因果世界模型的形式定义，关联相关领域工作并阐明其组件可从数据恢复的条件，为世界模型奠定支持因果推理与决策的基础。

A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces

一种用于腔隙和扩大血管周围空间联合检测的统一框架

Lucas He, Krinos Li, Hanyuan Zhang, Runlong He, Silvia Ingala, Luigi Lorenzini, Marleen de Bruijne, Frederik Barkhof, Rhodri Davies, Carole Sudre

机构 * Hawkes Institute, University College London, UK Unit for Lifelong Health ； Aging, University College London, UK Bioengineering Department ； Imperial-X, Imperial College London, UK Department of Diagnostic Radiology, Copenhagen University Hospital, Denmark Department of Radiology \& Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, The Netherlands Department of Radiology ； Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands Queen Square Institute of Neurology, University College London, UK Institute of Cardiovascular Sciences, University College London, UK Barts Heart Centre, St Bartholomew's Hospital, London, UK 纠错

AI总结 本文提出了一种统一框架，通过形态解耦和混合监督策略，提高EPVS和腔隙的联合检测性能，并在大规模数据集上验证了其鲁棒性。

Comments Accepted to the 29th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2026)