Utilizing a transfer model to classify epithelium and stroma on digital histopathological images for ovarian cancer patients
Xuxin Chen, Roy Zhang, Kar‐Ming Fung, Hong Liu, Bin Zheng, Yuchen Qiu · 2020
Automatic classification of epithelium and stroma regions on histopathological images is critically important in digital pathology. Although many studies have been conducted in this research area, few investigations have been focused on model generalizability between different types of tissue samples. The objective of this study is to initially verify the classification effectiveness of a sufficiently optimized transfer model. Accordingly, two datasets were assembled, which contain 157 breast cancer images (Dataset I) and 11 ovarian cancer images (Dataset II), respectively. A computer aided detection (CAD) scheme was developed for this classification task. The scheme first divided each image into small regions of interest (ROI) containing only epithelium or stroma tissues, using multi-resolution super-pixel algorithm. Then, a total of 26 quantitative features were computed for each ROI, which were used as the input of five different machine learning classifiers, namely, linear support vector machine (SVM), linear discriminant analysis (LDA), logistic regression, decision tree and k-nearest neighbors (KNN). The scheme was trained and optimized on Dataset I, and five-fold cross validation strategy was utilized for performance evaluation. After the scheme was sufficiently optimized on Dataset I, it was applied “as is” on dataset II. The results of the breast cancer dataset show that linear SVM achieved the highest classification accuracy of 0.910. When applied on the 11 ovarian cancer cases (Dataset II), the SVM model achieved an average classification accuracy of 0.744. This preliminary study initially demonstrates the model transfer performance for epithelium-stroma classification task.