Automatic Glioma Grading Based on Two-Stage Networks by Integrating Pathology and MRI Images

摘要

Glioma with a high incidence is one of the most common brain cancers. In the clinic, pathologist diagnoses the types of the glioma by observing the whole-slide images (WSIs) with different magnifications, which is time-consuming, laborious, and experience-dependent. The automatic grading of the glioma based on WSIs can provide aided diagnosis for clinicians. This paper proposes two fully convolutional networks, which are respectively used for WSIs and MRI images to achieve the automatic glioma grading (astrocytoma (lower-grade A), oligoden-droglioma (middle-grade 0), and glioblastoma (higher-grade G)). The final classification result is the probability average of the two networks. In the clinic and also in our multi-modalities image representation, grade A and O are difficult to distinguish. This work proposes a two-stage training strategy to exclude the distraction of the grade G and focuses on the classification of grade A and O. The experimental result shows that the proposed model achieves high glioma classification performance with the balanced accuracy of 0.889, Cohen's Kappa of 0.903, and Fl-score of 0.943 tested on the validation set.