Abstract
Pathologists visually observe hematoxylin-eosin (HE) stained images under a microscope to diagnose the presence or absence of tumors. However, variations exist in stained shades because of different HE staining methods. Such differences limit improvements in diagnosis. Conventional machine learning techniques create classifiers with features designed by humans. However, designing effective features for identifying tumor tissue requires a lot of time. This study proposes a method of automatically identifying the presence or absence of a tumor in a pathological image. The method consists of three steps: 1. pathological images with different stained shades are automatically classified by performing axial transformation by principal component analysis, 2. a classifier is created with a convolutional neural network (CNN) for each image group, 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 gastric moderately differentiated adenocarcinoma show improved identification accuracy.
