Summary of Conditional Diffusion Model For Longitudinal Medical Image Generation, by Duy-phuong Dao et al.
Conditional Diffusion Model for Longitudinal Medical Image Generation
by Duy-Phuong Dao, Hyung-Jeong Yang, Jahae Kim
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel diffusion-based model for generating 3D longitudinal medical imaging data from single magnetic resonance imaging (MRI) scans. The model addresses issues with missing data, irregular follow-up intervals, and varying observation periods by incorporating conditioning MRI and time-visit encoding. Experimental results show that the proposed method produces higher-quality images compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research aims to develop a system for creating 3D medical imaging data over time. This helps track changes in Alzheimer’s disease progression. The team designed a special model that fixes common problems with this type of data, like missing information or different amounts of observation time. They tested the new method and found it works better than others. |
Keywords
» Artificial intelligence » Diffusion
