Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans
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Abstract (arXiv)
The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild cognitive impairment by 2060. In response to this urgent call, methods for early detection of Alzheimer's disease have been developed for prevention and pre-treatment. Notably, literature on the application of deep learning in the automatic detection of the disease has been proliferating. This study builds upon previous literature and maintains a focus on leveraging multi-modal information to enhance automatic detection. We aim to predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive Impairment (MCI), and Alzheimer's Disease (AD), based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.
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References · 6
- 2023Multimodal Deep Learningcited by 257
- 2021TransMed: Transformers Advance Multi-modal Medical Image Classificationcited by 86
- 2016Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networkscited by 24
- 2019Deep Surface Normal Estimation with Hierarchical RGB-D Fusioncited by 20
- 2020Modality Compensation Network: Cross-Modal Adaptation for Action Recognitioncited by 16
- 2019On the design of convolutional neural networks for automatic detection of Alzheimer's diseasecited by 12
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