Fuchun Liu
ABSTRACT
Preoperative glioma grading is important to patient's follow-up treatment and prognosis. In recent years, deep learning technology has developed rapidly in computer vision. Various medical images have enabled deep learning to be widely used in computer-aided diagnosis (CAD). Typically, these methods fuse multi-modal image information or multi-model structures. They only use the encoder's final feature map representation output for classification. In this paper, ResNet and graph convolutional network (GCN) are cascaded. Then the hierarchical feature map of the encoder is used to construct the graph topology. Finally, GCN analyzes the graph topology to achieve case-level glioma classification. Our model achieved an F1-score of 0.907 and an average test accuracy of 93.74%. It outperforms the traditional convolutional neural network (CNN) model.
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Index Terms
- Preoperative Glioma Grading Based on Hierarchical Information Using Residual Network and Graph Convolutional Network
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