Improving the generalization of disease stage classification with deep CNN for Glioma histopathological images
Asami Yonekura, Hiroharu Kawanaka, V. B. Surya Prasath, Bruce J. Aronow, Haruhiko Takase · 2017
In the field of histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help define precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. One of the severe brain tumors is the Glioma can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of the tedious manual evaluation required for efficient decision-making and diagnosis. Therefore, there is a strong demand for quick and automatic analysis to do that. In this study, we consider feature extraction and disease stage classification for Glioma images using automatic image analysis methods with deep learning techniques. By devising a custom made deep convolutional neural network (CNN) for disease stage classification we apply it on image data available on the cancer genome atlas for brain glioma in histopathology.