Detection and classification of breast cancer in whole slide histopathology images using deep convolutional networks

Abstract

The most frequent non-skin cancer type is breast cancer which is also named one of the most deadliest diseases where early and accurate diagnosis is critical for recovery. Recent medical image processing researches have demonstrated promising results that may contribute to the analysis of biopsy images by enhancing the understanding or by revealing possible unhealthy tissues during diagnosis. However, these studies focused on well-annotated and -cropped patches, whereas a fully automated computer-aided diagnosis (CAD) system requires whole slide histopathology image (WSI) processing which is, in fact, enormous in size and, therefore, difficult to process with a reasonable computational power and time. Moreover, those whole slide biopsies consist of healthy, benign and cancerous tissues at various stages and thus, simultaneous detection and classiffication of diagnostically relevant regions are challenging. We propose a complete CAD system for efficient localization and classification of regions of interest (ROI) in WSI by employing state-of-the-art deep learning techniques. The system is developed to resemble organized work ow of expert pathologists by means of progressive zooming into details, and it consists of two separate sequential steps: (1) detection of ROIs in WSI, (2) classification of the detected ROIs into five diagnostic classes. The novel saliency detection approach intends to mimic efficient search patterns of experts at multiple resolutions by training four separate deep networks with the samples extracted from the tracking records of pathologists' viewing of WSIs. The detected relevant regions are fed to the classification step that includes a deeper network that produces probability maps for classes, followed by a post-processing step for final diagnosis In the experiments with 240 WSI, the proposed saliency detection approach outperforms a state-of-the-art method by means of both efficiency and eectiveness, and the final classification of our complete system obtains slightly lower accuracy than the mean of 45 pathologists' performance. According to the Mc- Nemar's statistical tests, we cannot reject that the accuracies of 32 out of 45 pathologists are not different from the proposed system. At the end, we also provide visualizations of our deep model with several advanced techniques for better understanding of the learned features and the overall information captured by the network