Breast Cancer Histology Image Classification using Deep Learning
Canh Nguyen, Anh H. Vo, Bao T. Nguyen · 2019
The breast cancer histology image classification task is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. A common challenge of the breast cancer histology image classification, also in other medical domain, is a lack of sufficient data. To address the limitations of the small amount of data, we applied additional patch extraction (APE) of whole-slide image (WSI) as dataset extension approach. Besides, to improve the classification accuracy, we proposed to use the test time augmentation (TTA) technique (with horizontal/vertical flipping, and ±90 degree rotation) upon the original convolution neural network (CNN) so that model is able to make a better decision on several breast cancer testing images instead of single prediction. Experiments was done by applying APE on ICIAR 2018 dataset to get the extended one (ICIAR-EXT) which is publicly available at https://github.com/canhnp/ICIAR-EXT. Moreover, when applying TTA technique with CNN on ICIAR-EXT at testing state, the accuracy has achieved a satisfactory result of 78% for 4-class breast cancer classification for 100 unlabeled ICIAR 2018 test set.