Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
This addresses the time-consuming and error-prone manual analysis of histology images for breast cancer detection, but it is incremental as it builds on existing CNN methods.
The authors tackled automated classification of breast cancer histology images into four pathologies using a hierarchical CNN system, achieving 0.99 accuracy on a BACH dataset test split and 0.81 accuracy on the BACH challenge test, ranking 8th out of 51 teams.
Microscopic histology image analysis is a cornerstone in early detection of breast cancer. However these images are very large and manual analysis is error prone and very time consuming. Thus automating this process is in high demand. We proposed a hierarchical system of convolutional neural networks (CNN) that classifies automatically patches of these images into four pathologies: normal, benign, in situ carcinoma and invasive carcinoma. We evaluated our system on the BACH challenge dataset of image-wise classification and a small dataset that we used to extend it. Using a train/test split of 75%/25%, we achieved an accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that of the extension. On the test of the BACH challenge, we've reached an accuracy of 0.81 which rank us to the 8th out of 51 teams.