Summary of Multi-modal Imaging Genomics Transformer: Attentive Integration Of Imaging with Genomic Biomarkers For Schizophrenia Classification, by Nagur Shareef Shaik et al.
Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification
by Nagur Shareef Shaik, Teja Krishna Cherukuri, Vince D. Calhoun, Dong Hye Ye
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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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 proposed Multi-modal Imaging Genomics Transformer (MIGTrans) model is a novel approach to diagnose schizophrenia by integrating genomic features with structural and functional imaging data. By attentively combining these modalities, MIGTrans achieves an accuracy of 86.05% (+/- 0.02), outperforming existing methods. The model identifies significant genomic locations and brain morphological/connectivity patterns associated with the disorder. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new tool called MIGTrans helps doctors diagnose schizophrenia more accurately by combining genetic information with images of the brain. This approach is better than just using brain scans alone, which have been used in the past to try to identify people with this serious mental illness. MIGTrans can help doctors better understand what’s going on in the brains of people with schizophrenia and could eventually lead to more effective treatments. |
Keywords
» Artificial intelligence » Multi modal » Transformer
