Cross-Modal Transfer Learning Methods for Alzheimer's Disease Diagnosis

Area of Science:

• Neuroimaging
• Artificial Intelligence
• Medical Diagnostics

Background:

• Alzheimer's disease (AD) diagnosis relies on complex imaging techniques.
• Convolutional Neural Networks (CNNs) show promise in analyzing medical images.
• Integrating multiple imaging modalities can improve diagnostic performance.

Purpose of the Study:

• To investigate the efficacy of cross-modal transfer learning for Alzheimer's disease detection.
• To compare transfer learning performance against single-modality and multimodal approaches.

Main Methods:

• Utilized Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) brain scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
• Trained CNNs on one imaging modality and fine-tuned on another (cross-modal transfer learning).
• Compared performance against CNNs trained from scratch on single modalities and traditional multimodal methods.

Main Results:

• Cross-modal transfer learning significantly outperformed CNNs trained from scratch on individual modalities.
• Transfer learning approaches demonstrated superior performance compared to multimodal methods using the same dataset.
• This highlights the effectiveness of leveraging information across different imaging types.

Conclusions:

• Cross-modal transfer learning is a powerful technique for Alzheimer's disease detection.
• This method offers improved diagnostic accuracy over single-modality and conventional multimodal analyses.
• Future research should explore further applications of transfer learning in neurodegenerative disease diagnostics.