Summary of Biological Brain Age Estimation Using Sex-aware Adversarial Variational Autoencoder with Multimodal Neuroimages, by Abd Ur Rehman et al.
Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages
by Abd Ur Rehman, Azka Rehman, Muhammad Usman, Abdullah Shahid, Sung-Min Gho, Aleum Lee, Tariq M. Khan, Imran Razzak
First submitted to arxiv on: 7 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 The paper proposes a novel multimodal framework for biological brain age estimation using sex-aware adversarial variational autoencoders (SA-AVAEs). The framework integrates adversarial and variational learning to disentangle latent features from structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data. This allows the model to capture modality-specific and shared information, as well as sex-specific aging patterns. The authors evaluate their model using the OpenBHB dataset and demonstrate its robustness across various age groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use brain scans to figure out how old someone’s brain is. Brain age can be an important sign of whether someone has certain diseases like Alzheimer’s. Right now, we have two types of brain scans: ones that show the structure of the brain (sMRI) and ones that show which parts are working (fMRI). The problem is that fMRI scans can be noisy and make it hard to combine them with sMRI scans. The researchers developed a special computer program called a sex-aware adversarial variational autoencoder (SA-AVAE) that can handle both types of scans and even take into account the person’s sex. They tested their program on a big dataset and found that it works better than other methods. |
Keywords
» Artificial intelligence » Variational autoencoder
