Tumor Region Localization in H&E Breast Carcinoma Images Using Deep Convolutional Neural Network

Digital pathology incorporates the acquisition, management, sharing and interpretation of pathology information in a digital environment. The field of digital pathology is currently regarded as one of the most promising avenues of diagnostic medicine. Many computer-aided detection and diagnostic algorithms has been developed to assist pathologists in their daily clinical routine, with varying degree of success. These include cell detection and counting, tissue classification and cancer grading, among others. Deep learning, or more specifically, deep convolutional neural network, is a machine learning algorithm that has also gained a lot of attention recently due to their ability to achieve state-of-the-art accuracy. In this paper we have constructed and expanded the deep model network to localize tumor regions in histology images of breast carcinoma. We proposed our own deep convolutional neural network with lesser hardware requirement using 64×64×3 input patch. Our proposed method is able to provide reliable tumor region localization, visually and objectively, based on very limited training dataset.