Summary of Triamese-vit: a 3d-aware Method For Robust Brain Age Estimation From Mris, by Zhaonian Zhang and Richard Jiang
Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs
by Zhaonian Zhang, Richard Jiang
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 research paper presents a novel approach to brain age estimation using Vision Transformers (ViTs) in combination with three-dimensional Magnetic Resonance Imaging (MRI) scans. The authors introduce Triamese-ViT, an innovative adaptation of ViT that combines the strengths of 3D convolutional neural networks (CNNs) and ViTs for more accurate and interpretable brain age estimation. Tested on a dataset of 1351 MRI scans, Triamese-ViT achieves significant improvements in Mean Absolute Error (MAE), Spearman correlation coefficient with chronological age, and brain age gap (BAG). The paper highlights the potential of this approach for in-depth brain age analysis and disease diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special machines to look at people’s brains and figure out how old they are based on what they see. This is important because some diseases only happen when you’re a certain age. The researchers used a new way to use these machines, called Vision Transformers, which are good at understanding pictures. They combined this with other techniques to make it work even better. When they tested their method on lots of brain scans, it was really accurate and could tell how old someone’s brain is compared to how old they are in real life. This could help doctors understand more about what’s happening in people’s brains as they get older and develop new treatments for age-related diseases. |
Keywords
* Artificial intelligence * Mae * Vit
