Summary of Multi-task Adversarial Variational Autoencoder For Estimating Biological Brain Age with Multimodal Neuroimaging, by Muhammad Usman et al.
Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging
by Muhammad Usman, Azka Rehman, Abdullah Shahid, Abd Ur Rehman, Sung-Min Gho, Aleum Lee, Tariq M.Khan, Imran Razzak
First submitted to arxiv on: 15 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 The Multitask Adversarial Variational Autoencoder (M-AVAE) is a custom deep learning framework designed to improve brain age predictions by integrating structural and functional MRI data. The model separates latent variables into generic and unique codes, isolating shared and modality-specific features. By incorporating multitask learning with sex classification as an additional task, the model captures sex-specific aging patterns. In evaluation on the OpenBHB dataset, M-AVAE achieves a mean absolute error of 2.77 years, outperforming traditional methods. This achievement positions M-AVAE as a powerful tool for metaverse-based healthcare applications in brain age estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program called M-AVAE can better guess how old someone’s brain is based on special scans called MRI. This program uses information from two types of MRI scans: one that shows the structure of the brain and another that shows how different parts of the brain are connected. The program does this by using a special technique to separate out information that is important for guessing brain age from information that is not as important. By doing this, M-AVAE can better understand how brains change with age in men and women differently. When tested on a large collection of MRI scans, M-AVAE was able to guess brain age more accurately than other methods. |
Keywords
» Artificial intelligence » Classification » Deep learning » Variational autoencoder
