This paper proposes a deep neural network, Geographic Attention Model (GA-Net), for body composition tissue segmentation. By adding an auxiliary body area prediction task, our method exploits the rich semantic and spatial features contained in the body area and incorporates the features of both area and body composition tissue. In this way, GA-Net achieves superior performance for body composition tissue segmentation, especially for the indistinguishable boundaries of multiple tissues. And the enhanced representation ability of GA-Net also allows GA-Net to obtain well generalization performance on the limited dataset.
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