USING DEEP LEARNING ALGORITHMS FOR BREAST CANCER DIAGNOSIS IN PATHOLOGY

Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. We propose a deep learning architecture, based on VGG-16 network for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks by dividing whole-slide images on 50x50 pixel histology image patches. We aim to learn discriminant compact features at beginning of our deep convolutional neural network.