Summary of Mrgen: Segmentation Data Engine For Underrepresented Mri Modalities, by Haoning Wu et al.
MRGen: Segmentation Data Engine For Underrepresented MRI Modalities
by Haoning Wu, Ziheng Zhao, Ya Zhang, Yanfeng Wang, Weidi Xie
First submitted to arxiv on: 4 Dec 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 tackles the challenge of training medical image segmentation models for rare MRI modalities with limited annotated data. Generative models are used to synthesize training data and train segmentation models on underrepresented modalities. The contributions include a large-scale radiology dataset (MRGen-DB) with rich metadata, a diffusion-based data engine (MRGen) that generates realistic images from text prompts and segmentation masks, and extensive experiments demonstrating improved segmentation performance on unannotated modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using special computer models to help doctors train their computers to identify important features in medical scans. Right now, it’s hard to get these computers trained because there aren’t enough labeled pictures of the things they’re supposed to find. The researchers created a big database with lots of different medical images and some tools to make more fake images that can be used for training. They tested their ideas on many different types of scans and showed that it works really well. |
Keywords
» Artificial intelligence » Diffusion » Image segmentation
