Sampling Based Tumor Recognition in Whole-Slide Histology Image With Deep Learning Approaches
Yiqing Shen, Jing Ke
Abstract
Histopathological identification of tumor tissue is one of the routine pathological diagnoses for pathologists. Recently, computational pathology has been successfully interpreted by a variety of deep learning-based applications. Nevertheless, the high-efficient and spatial-correlated processing of individual patches have always attracted attention in whole-slide image (WSI) analysis. In this paper, we propose a high-throughput system to detect tumor regions in colorectal cancer histology slides precisely. We train a deep convolutional neural network (CNN) model and design a Monte Carlo (MC) adaptive sampling method to estimate the most representative patches in a WSI. Two conditional random field (CRF) models are designed, namely the correction CRF and the prediction CRF are integrated for spatial dependencies of patches. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) to evaluate the performance of the system. The overall diagnostic time can be reduced from 56.7 percent to 71.7 percent on the slides of a varying tumor distribution, with an increase in classification accuracy.