Tumor detection and whole slide classification of H&E lymph node images using convolutional neural network

Histopathological analysis of tissues has been gaining a lot of interests recently, from developing computer algorithms to assist pathologists from cell detection and counting, to tissue classification and cancer grading. With the advent of whole slide imaging, the field of digital pathology has gained enormous popularity, and is currently regarded as one of the most promising avenues of diagnostic medicine. Deep learning advancement on image set today has successfully evolved as many models has been proposed and produced state-of-the-art object classifying results. This is not limited to large database such as Imagenet but also has seen applications in other medical image analysis related areas. In this paper we have carefully constructed and expanded the deep model network to classify normal and tumor slides in histology images of lymph nodes tissue. We have proposed our own deep learning model based on convolutional neural network with smaller requirement using 64×64×3 input image with 12 convolutional layer with max pooling and ReLU activation function. Our method has better AUC result at 0.94 than the winner of Camelyon16 Challenge with AUC of 0.925.