Deep Learning in Big Image Data: Histology IMage Classification for Breast Cancer Diagnosis

Abstract

This paper present results of the use of Deep Learning approach and Convolutional Neural Networks (CNN) for the problem of breast cancer diagnosis. Specifically, the main goal of this particular study was to detect and to segment (i.e. delineate) regions of micro- and macro- metastases in whole-slide images of lymph node sections. The whole-slide imaging of tissue probes produces very large histological images. The size of resultant color RGB images typically ranges between 50 000х50 000 and 200 000x200 000 pixels and they considered as a basic component of computerized methods in recent Digital Pathology. Original hematoxylin and eosin stained whole- slide images produced by two different optical microscope scanners were kindly provided by founders of CAMELYON16 world-wide competition.