Abstract
AbstractHistopathology is diagnosis based on visual examination of tissue sections under a microscope. With the growing number of digitally scanned tissue slide images, computer‐based segmentation and classification of these images is a high‐demand area of research. Convolutional neural networks (CNNs) constitute the most popular classification architecture for a variety of image classification problems. However, applying CNNs to histology slides is not a trivial task and has several challenges, ranging from variations in the colors of slides to excessive high resolution and lack of proper labeling. In this advanced review, we introduce the application of CNN‐based architectures to digital histological image analysis, discuss some problems associated with such analysis, and look at possible solutions.This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning
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