Vision-language models have demonstrated impressive capabilities in generating 2D images under various conditions; however, the success of these models is largely enabled by extensive, readily available pretrained foundation models. Critically, comparable pretrained models do not exist for 3D, significantly limiting progress. As a result, the potential of vision-language models to produce high-resolution 3D counterfactual medical images conditioned solely on natural language remains unexplored. Addressing this gap would enable powerful clinical and research applications, such as personalized counterfactual explanations, simulation of disease progression, and enhanced medical training by visualizing hypothetical conditions in realistic detail. Our work takes a step toward this challenge by introducing a framework capable of generating high-resolution 3D counterfactual medical images of synthesized patients guided by free-form language prompts. We adapt state-of-the-art 3D diffusion models with enhancements from Simple Diffusion and incorporate augmented conditioning to improve text alignment and image quality. To our knowledge, this is the first demonstration of a language-guided native-3D diffusion model applied to neurological imaging, where faithful three-dimensional modeling is essential. On two neurological MRI datasets, our framework simulates varying counterfactual lesion loads in Multiple Sclerosis and cognitive states in Alzheimer's disease, generating high-quality images while preserving subject fidelity. Our results lay the groundwork for prompt-driven disease progression analysis in 3D medical imaging. Project link - https://lesupermomo.github.io/imagining-alternatives/.
LD-ViCE: Latent Diffusion Model for Video Counterfactual Explanations
Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau（莱茵-瓦尔德大学凯泽斯劳滕-兰道分校）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Control
Institute of Medical Engineering, MIT（麻省理工学院医学工程研究所）；Brigham and Women's Hospital, Harvard（哈佛大学布莱尔妇女医院）；HeartFlow（HeartFlow公司）；Showa University（昭和大学）；OLV Aalst
Protein Counterfactuals via Diffusion-Guided Latent Optimization
Machine Learning Group, Technische Universität Berlin（技术大学柏林机器学习组）；Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所（BIFOLD））；BASF Digital Solutions GmbH（巴斯夫数字解决方案有限公司）
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study
organization= Department of Physics \& Astronomy, University of British Columbia , city= Vancouver , state= BC , country= Canada；organization= Department of Integrative Oncology, BC Cancer Research Institute , city= Vancouver , state= BC , country= Canada；organization= Department of Radiology, University of British Columbia , city= Vancouver , state= BC , country= Canada
Physically Plausible Video Generation via Visual-Semantic Chain-of-Events Conditioning
College of Electronics and Information Engineering, Sichuan University（四川大学电子信息学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Computer Science, University of Adelaide（阿德莱德大学计算机学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
