  Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that
often progresses from Mild Cognitive Impairment (MCI), leading to memory loss
and significantly impacting patients' lives. Clinical trials indicate that
early targeted interventions for MCI patients can potentially slow or halt the
development and progression of AD. Previous research has shown that accurate
medical classification requires the inclusion of extensive multimodal data,
such as assessment scales and various neuroimaging techniques like Magnetic
Resonance Imaging (MRI) and Positron Emission Tomography (PET). However,
consistently tracking the diagnosis of the same individual over time and
simultaneously collecting multimodal data poses significant challenges. To
address this issue, we introduce GFE-Mamba, a classifier based on Generative
Feature Extraction (GFE). This classifier effectively integrates data from
assessment scales, MRI, and PET, enabling deeper multimodal fusion. It
efficiently extracts both long and short sequence information and incorporates
additional information beyond the pixel space. This approach not only improves
classification accuracy but also enhances the interpretability and stability of
the model. We constructed datasets of over 3000 samples based on the
Alzheimer's Disease Neuroimaging Initiative (ADNI) for a two-step training
process. Our experimental results demonstrate that the GFE-Mamba model is
effective in predicting the conversion from MCI to AD and outperforms several
state-of-the-art methods. Our source code and ADNI dataset processing code are
available at https://github.com/Tinysqua/GFE-Mamba.
