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Abstract #2351

Deep Cross-Modal Feature Learning and Fusion for Early Dementia Diagnosis

Tao Zhou1, Kim-Han Thung1, and Dinggang Shen1

1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

Studies have shown that neuroimaging data (e.g., MRI, PET) and genetic data (e.g., SNP) are associated with the Alzheimer’s Disease (AD). However, to achieve a more accurate AD diagnosis model using these data is challenging, as these data are heterogeneous and high-dimensional. Thus, we first used region-of-interest based features and deep feature learning to reduce the dimension of the neuroimaging and SNP data, respectively. Then we proposed a deep cross-modal feature learning and fusion framework to fuse the high-level features of these data. Experimental results show that our method using MRI+PET+SNP data outperforms other comparison methods.

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