Automatic disease stage classification of glioblastoma multiforme histopathological images using deep convolutional neural network.
10.1007/s13534-018-0077-0
- Author:
Asami YONEKURA
1
;
Hiroharu KAWANAKA
;
V B SURYA PRASATH
;
Bruce J ARONOW
;
Haruhiko TAKASE
Author Information
1. Graduate School of Engineering, Mie University, 1577 Kurima-machiya, Tsu, Mie 514-8507, Japan.
- Publication Type:Original Article
- Keywords:
Deep learning;
Histopathology;
Image analysis;
Glioblastoma multiforme;
Classification;
Convolutional neural network
- MeSH:
Classification*;
Diagnosis;
Diagnosis, Computer-Assisted;
Genome;
Glioblastoma*;
Glioma;
Methods;
Pathology;
Precision Medicine;
Subject Headings
- From:
Biomedical Engineering Letters
2018;8(3):321-327
- CountryRepublic of Korea
- Language:English
-
Abstract:
In the field of computational histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. In this work, we discuss an automatic feature extraction and disease stage classification method for glioblastoma multiforme (GBM) histopathological images. In this paper, we use deep convolutional neural 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 is undertaken. The experiments using Glioma images from The Cancer Genome Atlas shows that we obtain 96:5% average classification accuracy for our network and for higher cross validation folds other networks perform similarly with a higher accuracy of 98:0%. Deep CNNs could extract significant features from the GBM histopathology images with high accuracy. Overall, the disease stage classification of GBM from histopathological images with deep CNNs is very promising and with the availability of large scale histopathological image data the deep CNNs are well suited in tackling this challenging problem.
