Summary of Diamond: Dementia Diagnosis with Multi-modal Vision Transformers Using Mri and Pet, by Yitong Li et al.
DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET
by Yitong Li, Morteza Ghahremani, Youssef Wally, Christian Wachinger
First submitted to arxiv on: 30 Oct 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 proposes a novel framework, DiaMond, for diagnosing dementia, particularly Alzheimer’s Disease (AD) and frontotemporal dementia (FTD). The challenge lies in integrating magnetic resonance imaging (MRI), positron emission tomography (PET), and deep learning to overcome limitations of using single modalities. DiaMond uses vision Transformers to combine MRI and PET, incorporating self-attention, bi-attention, and multi-modal normalization. This approach significantly outperforms existing methods across various datasets, achieving 92.4% balanced accuracy in AD diagnosis, 65.2% for AD-MCI-CN classification, and 76.5% in differential diagnosis of AD and FTD. The framework’s robustness is validated through a comprehensive ablation study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better diagnose dementia by combining two types of medical scans: MRI and PET. Right now, these scans are usually used separately, but this new method called DiaMond combines them to get more accurate results. The researchers tested their method on several groups of people with different types of dementia and found that it worked much better than previous methods. This could lead to faster and more accurate diagnoses in the future. |
Keywords
» Artificial intelligence » Attention » Classification » Deep learning » Multi modal » Self attention
