Summary of Latent 3d Brain Mri Counterfactual, by Wei Peng et al.
Latent 3D Brain MRI Counterfactual
by Wei Peng, Tian Xia, Fabio De Sousa Ribeiro, Tomas Bosschieter, Ehsan Adeli, Qingyu Zhao, Ben Glocker, Kilian M. Pohl
First submitted to arxiv on: 9 Sep 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 addresses the challenge of generating diverse, high-quality brain MRI datasets using deep learning models. The authors propose a two-stage approach that leverages generative models and causal modeling to produce realistic MRI data. In the first stage, they use a Variational Quantum VAE (VQ-VAE) to learn a compact embedding of the MRI volume. Then, they integrate their causal model into this latent space and execute a three-step counterfactual procedure using a closed-form Generalized Linear Model (GLM). The authors demonstrate the effectiveness of their method on real-world high-resolution MRI data, showcasing its potential for generating high-quality 3D MRI counterfactuals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to generate brain MRI datasets that are currently too small to train deep learning models. They use special computer programs called generative models that can learn and copy the patterns in the MRI images. However, these programs struggle to make realistic images outside of what they’ve seen before. The authors suggest using another type of program, called a causal model, which is designed for situations where we want to imagine what would happen if something had been different. They combine these two approaches to create a new method that can generate high-quality brain MRI images. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Latent space
