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Asami Yonekura,Hiroharu Kawanaka,V. B. Surya Prasath,Bruce J. Aronow,Haruhiko Takase 대한의용생체공학회 2018 Biomedical Engineering Letters (BMEL) Vol.8 No.3
In the field of computational histopathology, computer-assisted diagnosis systems are important in obtaining patientspecificdiagnosis for various diseases and help precision medicine. Therefore, many studies on automatic analysis methodsfor digital pathology images have been reported. In this work, we discuss an automatic feature extraction and disease stageclassification method for glioblastoma multiforme (GBM) histopathological images. In this paper, we use deep convolutionalneural networks (Deep CNNs) to acquire feature descriptors and a classification scheme simultaneously. Further,comparisons with other popular CNNs objectively as well as quantitatively in this challenging classification problem isundertaken. The experiments using Glioma images from The Cancer Genome Atlas shows that we obtain 96:5% averageclassification accuracy for our network and for higher cross validation folds other networks perform similarly with a higheraccuracy of 98:0%. Deep CNNs could extract significant features from the GBM histopathology images with highaccuracy. Overall, the disease stage classification of GBM from histopathological images with deep CNNs is verypromising and with the availability of large scale histopathological image data the deep CNNs are well suited in tacklingthis challenging problem.
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