Efficient Grading of Prostate Cancer WSI with Deep Learning

The field of histopathology, which involves visual examination of tissue samples at a microscopic scale, is very important for the diagnosis of cancer. Although this task is currently performed by human experts, the design of computer vision-based systems to assist human experts is an interesting research area. This problem is ideal for the application of computer-based image analysis; especially, with the great success of convolutional neural networks (CNNs) in image segmentation and classification in the last decade. However, applying CNNs to this problem is challenging for a number of reasons, such as excessive high resolution (involving huge computational burden), variations in sample processing, and insufficient annotation. In this current work, we propose a CNN-based approach to tackle the problem of prostate cancer grading from Whole Slide Images (WSIs). We use a patch-based, multi-step training algorithm to address the challenges of large image size, tissue sample variations and partial annotation. Then we propose two novel classification strategies using an ensemble of CNN models to classify tissue slide images into different ISUP grades (1 - 5). We demonstrate the efficacy of our method on the publicly available large scale Prostate cANcer graDe Assessment (PANDA) Challenge dataset. The effectiveness of the technique is measured using Cohen's quadratic kappa score. The results are shown to be highly accurate (kappa score of 0.88) and better than other leading state-of-the art methods.