Tumor Tissue Identification Technology by Estimating Features of Immunostaining Images using Convolutional Neural Networks

Abstract

Pathologists visually observe hematoxylin-eosin (HE) stained images under a microscope to perform pathological diagnosis. If it is not possible to sufficiently diagnose by judging shape using HE stained specimens alone, it is necessary to add another evaluation method such as immunohistochemistry (immunostaining)．In order to accurately and rapidly identify a tumor, this study proposes a method of automatically identifying a tumor in a pathological image by estimating features of immunostaining from an HE stained image. The method consists of three steps: 1. features of tumor presence or absence are extracted from the HE stained image using a convolutional neural network (CNN), 2. a classifier is created so that the features obtained from the HE stained image approach the features of the presence or absence of a tumor stained by immunostaining by using the CNN, and 3. the presence or absence of a tumor is judged by using the classifier. The experimental results using digital images of pathological tissue specimens of prostate cancer show improved identification accuracy.