Multi-classification of Breast Cancer Histology Images by Using a Fine-Tuning Strategy

Abstract

The adoption of automatic systems to support the diagnosis of breast cancer from histology images analysis is rapidly becoming more widespread. Most of the works in literature focus principally on a two-class problem, namely benign and malignant tumors. However, the development of multi-classification approaches would also be greatly appreciated in order to support the determination of an ideal therapeutic schedule for the treatment of breast cancer. The multi-classification of histology images is particularly challenging due to the broad variability of appearance of the image, the great differences in the spatial arrangement of the histological structures, and the heterogeneity in the color distribution. In this work, a fine-tuning strategy of ResNet, a residual convolutional neural network, is presented to address the problem of multi-classification for breast cancer histology images in normal tissue, benign lesions, in situ carcinomas and invasive carcinomas. We have combined three configurations of ResNet, differing from each other in terms of the number of layers, by using a maximum probability rule to balance out their individual weaknesses during the testing. The proposed approach achieved a remarkable performance on the images provided for the Grand Challenge on Breast Cancer Histology Images (BACH), within the context of the International Conference ICIAR 2018.