Pseudo-3D CNN with inter-slice attention for glioma grading

Gliomas comprise around 80 percent of all malignant brain tumors which can be further classified into the low-grade glioma and high-grade glioma categories. Compared to low-grade gliomas, high-grade gliomas show more malignant behavior since they usually grow rapidly and frequently destroy healthy brain tissue. Therefore, it is important to determine the malignancy of gliomas for initial treatment plan. Most existing methods are based on radiomics or transfer learning which extract features from single slice without considering that 3D features between adjacent slices can provide stronger discriminative power. In this work, we propose to incorporate the attention mechanism and pseudo-3d module into a deep convolutional network architecture, which can reduce model size and produce fine features for glioma grading. Our work focuses on the inter-slice relationship and propose an attention unit, named “Inter-Slice Attention Module”, which adaptively refines intermediate feature maps by modelling dependencies between adjacent slices. We evaluate our method on an open dataset of gliomas, achieving mean accuracy of 89.47%.