Summary of Medical Video Generation For Disease Progression Simulation, by Xu Cao et al.
Medical Video Generation for Disease Progression Simulation
by Xu Cao, Kaizhao Liang, Kuei-Da Liao, Tianren Gao, Wenqian Ye, Jintai Chen, Zhiguang Ding, Jianguo Cao, James M. Rehg, Jimeng Sun
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel Medical Video Generation (MVG) framework that enables controlled manipulation of disease-related image and video features. The approach leverages large language models to generate recaption prompts for disease trajectories, followed by a controllable multi-round diffusion model simulating the disease progression state for each patient. A diffusion-based video transition generation model then interpolates disease progression between these states. This framework is validated across three medical imaging domains: chest X-ray, fundus photography, and skin image. The results demonstrate that MVG outperforms baseline models in generating coherent and clinically plausible disease trajectories. Additionally, two user studies conducted by veteran physicians provide further validation and insights into the clinical utility of the generated sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a tool that helps doctors predict how diseases will progress over time. This is important because it’s hard to get lots of medical images from individual patients. The new framework uses big language models to come up with ideas for what disease progression might look like, then generates realistic videos showing how the disease changes over time. It works across different types of medical images and can even fill in missing data. Doctors think this could be a really useful tool for predicting diseases and helping them make decisions. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
