Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks.

Content Provider	Europe PMC
Author	Teramoto, Atsushi Tsukamoto, Tetsuya Michiba, Ayano Kiriyama, Yuka Sakurai, Eiko Imaizumi, Kazuyoshi Saito, Kuniaki Fujita, Hiroshi
Editor	Chen, Dechang
Copyright Year	2022
Abstract	Interstitial pneumonia of uncertain cause is referred to as idiopathic interstitial pneumonia (IIP). Among the various types of IIPs, the prognosis of cases of idiopathic pulmonary fibrosis (IPF) is extremely poor, and accurate differentiation between IPF and non-IPF pneumonia is critical. In this study, we consider deep learning (DL) methods owing to their excellent image classification capabilities. Although DL models require large quantities of training data, collecting a large number of pathological specimens is difficult for rare diseases. In this study, we propose an end-to-end scheme to automatically classify IIPs using a convolutional neural network (CNN) model. To compensate for the lack of data on rare diseases, we introduce a two-step training method to generate pathological images of IIPs using a generative adversarial network (GAN). Tissue specimens from 24 patients with IIPs were scanned using a whole slide scanner, and the resulting images were divided into patch images with a size of 224 × 224 pixels. A progressive growth GAN (PGGAN) model was trained using 23,142 IPF images and 7817 non-IPF images to generate 10,000 images for each of the two categories. The images generated by the PGGAN were used along with real images to train the CNN model. An evaluation of the images generated by the PGGAN showed that cells and their locations were well-expressed. We also obtained the best classification performance with a detection sensitivity of 97.2% and a specificity of 69.4% for IPF using DenseNet. The classification performance was also improved by using PGGAN-generated images. These results indicate that the proposed method may be considered effective for the diagnosis of IPF.
Journal	Diagnostics (Basel, Switzerland)
Volume Number	12
PubMed Central reference number	PMC9777207
Issue Number	12
PubMed reference number	36553202
e-ISSN	20754418
DOI	10.3390/diagnostics12123195
Language	English
Publisher	MDPI
Publisher Date	2022-12-16
Access Restriction	Open
Rights License	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). © 2022 by the authors.
Subject Keyword	idiopathic interstitial pneumonias classification convolutional neural network generative adversarial networks
Content Type	Text
Resource Type	Article
Subject	Clinical Biochemistry