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Abstract #2426

Multimodal Image Fusion Integrating Tensor Modeling and Deep Learning

Wenli Li1, Ziyu Meng1, Ruihao Liu1, Zhi-Pei Liang2,3, and Yao Li1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 3Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Multimodal brain imaging acquires complementary information of the brain. However, due to the high dimensionality of the data, it is challenging to capture the underlying joint spatial and cross-modal dependence required for statistical inference in various brain image processing tasks. In this work, we proposed a new multimodal image fusion method that synergistically integrates tensor modeling and deep learning. The tensor model was used to capture the joint spatial-intensity-modality dependence and deep learning was used to fuse spatial-intensity-modality information. Our method has been applied to multimodal brain image segmentation, producing significantly improved results.

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