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Cancer of the brain and central nervous system (CNS) is one of the leading causes of death in the United States. Approximately 85-90% of all primary CNS tumors are brain tumors. Gliomas, the most prevalent kind of malignant brain tumor, contain uncontrollably proliferating cells. Despite the fact that they rarely spread to the spinal cord or other human organs, they grow quickly and can infiltrate healthy tissues. Early diagnosis of glioma subtypes, such as glioblastoma and oligodendroglioma, is clinically challenging; more importantly, it is critical due to the differences in treatment options, therapeutic responsiveness, and patient survival. Histopathological study of biopsy specimens is used to diagnose and classify brain tumors. The existing procedure is time-consuming, labor-intensive, and prone to human error. These drawbacks emphasize the importance of developing a fully automated technique for brain tumor categorization. To ensure diagnostic accuracy, efficiency and reduce the required time, the use of automated brain tumor grading systems is being increasingly explored. Development of automated techniques can assist neuropathologists in streamlining the clinical diagnostic tasks. In this study we propose a two stage attention based network to locate diagnostically relevant regions of interest and then to accurately categorize a cohort of brain tumor (glioma) histopathology images (N=203) into three sub-types: glioblastoma, oligodendroglioma, and astrocytoma. Unlike traditional methods in histopathology image analysis, which assume that each extracted patch from a whole-slide image has the same label regardless of whether or not all patches are tumorous, our technique determines the region of interest in an weakly supervised manner and uses the discovered regions for downstream analysis. Our proposed method outperforms a single-stage attention network, achieving balanced accuracy, F1-Micro, and Cohen Kappa score of 0.73, 0.67, and 0.82, respectively, on a held out test set (N=27 cases) as compared to 0.59, 0.66 and 0.43, respectively, for the single-stage network.
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