Glioblastoma multiforme tissue histopathology images based disease stage classification with deep CNN
Asami Yonekura, Hiroharu Kawanaka, V. B. Surya Prasath, Bruce J. Aronow, Haruhiko Takase · 2017
Recently, many feature extraction methods for histopathology images have been reported for automatic quantitative analysis. One of the severe brain tumors is the Glioblastoma multiforme (GBM) and histopathology tissue images 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 tedious manual evaluation traditionally required for efficient evaluation. In this study, we consider feature extraction and disease stage classification for brain tumor histopathology images using automatic image analysis methods. In particular, we utilized an automatic feature extraction and labeling for histopathology imagery data given by The Cancer Genome Atlas (TCGA) and checked the classification accuracy of disease stages in GBM tissue images using deep Convolutional Neural Network (CNN). Experimental results indicate promise in automatic disease stage classification and high level of accuracy were obtained for tested image data.