Multimodal 3D CNN model with GMU, gated self-attention, and sparsely gated MoE achieves up to 95.47% accuracy on NC vs AD using MRI and PET, with ablations showing MoE benefit.
Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks
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abstract
Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.
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Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET
Multimodal 3D CNN model with GMU, gated self-attention, and sparsely gated MoE achieves up to 95.47% accuracy on NC vs AD using MRI and PET, with ablations showing MoE benefit.