Feasibility of fully automated classification of whole slide images based on deep learning
10.4196/kjpp.2020.24.1.89
- Author:
Kyung Ok CHO
1
;
Sung Hak LEE
;
Hyun Jong JANG
Author Information
1. Department of Pharmacology, Seoul St. Mary's Hospital, Seoul 06591, Korea.
- Publication Type:Original Article
- Keywords:
Computational pathology;
Computer-aided diagnosis;
Convolutional neural network;
Digital pathology
- MeSH:
Adenocarcinoma;
Classification;
Dataset;
Diagnosis;
Learning;
Observer Variation;
Stomach
- From:The Korean Journal of Physiology and Pharmacology
2020;24(1):89-99
- CountryRepublic of Korea
- Language:English
-
Abstract:
Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.
