Brain Tumor Classification from Radiology and Histopathology using Deep Features and Graph Convolutional Network

In this paper, we address the problem of brain tumor classification from radiology and histopathology data. A coarse-to-fine classification approach is adopted using a combination of deep features and Graph Convolution Network (GCN). As a first coarse step, we use 3D CNN to detect Glioblastoma from MRI images. In order to infer about Astrocytoma and Oligodendroglioma, Whole Slide Images (WSI) are employed in the second stage. During this fine classification stage, 2D CNN features are extracted at two different (global and local) magnification levels. A graph is constructed with nodes in the space of concatenated global and local features. Edges are constructed from feature similarity and graph topology. Finally, GCN is used with normalized graph Laplacian to ensure better relation-aware-representation leading to more accurate classification. Experimental comparisons on the CPM-RadPath2020 challenge dataset clearly demonstrate the state-of-the-art performance of our proposed strategy. The code implementation is available at https://github.com/arijitde92/BrainTumorClassification.