GFE-Mamba: Mamba-based AD Multi-modal Progression Assessment via Generative Feature Extraction from MCI

TL;DR

GFE-Mamba is a novel multimodal classifier that uses generative feature extraction and advanced attention mechanisms to predict Alzheimer's progression from MCI, outperforming existing methods.

Contribution

The paper introduces GFE-Mamba, a new multimodal classification framework that effectively fuses MRI, PET, and assessment data for early AD progression prediction.

Findings

GFE-Mamba outperforms several leading methods in predicting MCI to AD progression.

The generative feature extractor compensates for missing modalities, enhancing classification accuracy.

The model demonstrates robust performance on a newly developed cross-temporal progression dataset.

Abstract

Alzheimer's Disease (AD) is a progressive, irreversible neurodegenerative disorder that often originates from Mild Cognitive Impairment (MCI). This progression results in significant memory loss and severely affects patients' quality of life. Clinical trials have consistently shown that early and targeted interventions for individuals with MCI may slow or even prevent the advancement of AD. Research indicates that accurate medical classification requires diverse multimodal data, including detailed assessment scales and neuroimaging techniques like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). However, simultaneously collecting the aforementioned three modalities for training presents substantial challenges. To tackle these difficulties, we propose GFE-Mamba, a multimodal classifier founded on Generative Feature Extractor. The intermediate features provided by this Extractor can compensate for the shortcomings of PET and achieve profound multimodal fusion in the classifier. The Mamba block, as the backbone of the classifier, enables it to efficiently extract information from long-sequence scale information. Pixel-level Bi-cross Attention supplements pixel-level information from MRI and PET. We provide our rationale for developing this cross-temporal progression prediction dataset and the pre-trained Extractor weights. Our experimental findings reveal that the GFE-Mamba model effectively predicts the progression from MCI to AD and surpasses several leading methods in the field. Our source code is available at https://github.com/Tinysqua/GFE-Mamba.