Summary of Gfe-mamba: Mamba-based Ad Multi-modal Progression Assessment Via Generative Feature Extraction From Mci, by Zhaojie Fang et al.
GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI
by Zhaojie Fang, Shenghao Zhu, Yifei Chen, Binfeng Zou, Fan Jia, Chang Liu, Xiang Feng, Linwei Qiu, Feiwei Qin, Jin Fan, Changbiao Chu, Changmiao Wang
First submitted to arxiv on: 22 Jul 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 proposed GFE-Mamba model is a multimodal classifier that aims to predict the progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD). By leveraging Generative Feature Extractor and its intermediate features, the model can compensate for the shortcomings of Positron Emission Tomography (PET) and achieve profound multimodal fusion. The Mamba block enables efficient extraction of information from long-sequence scale data, while Pixel-level Bi-cross Attention supplements pixel-level information from Magnetic Resonance Imaging (MRI) and PET. The authors provide a rationale for developing the cross-temporal progression prediction dataset and pre-trained Extractor weights. Experimental findings reveal that the GFE-Mamba model effectively predicts the progression from MCI to AD, outperforming several leading methods in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict whether someone with Mild Cognitive Impairment (MCI) will develop Alzheimer’s Disease (AD). It uses special tools called Generative Feature Extractor and Mamba block to analyze different types of data like MRI, PET, and memory tests. The goal is to create a better model that can accurately predict when someone with MCI will develop AD. The authors tested their model and found it was more accurate than other similar models. |
Keywords
» Artificial intelligence » Cross attention
