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

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 s...

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Autores principales: Teramoto, Atsushi, Tsukamoto, Tetsuya, Michiba, Ayano, Kiriyama, Yuka, Sakurai, Eiko, Imaizumi, Kazuyoshi, Saito, Kuniaki, Fujita, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777207/
https://www.ncbi.nlm.nih.gov/pubmed/36553202
http://dx.doi.org/10.3390/diagnostics12123195
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author Teramoto, Atsushi
Tsukamoto, Tetsuya
Michiba, Ayano
Kiriyama, Yuka
Sakurai, Eiko
Imaizumi, Kazuyoshi
Saito, Kuniaki
Fujita, Hiroshi
author_facet Teramoto, Atsushi
Tsukamoto, Tetsuya
Michiba, Ayano
Kiriyama, Yuka
Sakurai, Eiko
Imaizumi, Kazuyoshi
Saito, Kuniaki
Fujita, Hiroshi
author_sort Teramoto, Atsushi
collection PubMed
description 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.
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spelling pubmed-97772072022-12-23 Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks Teramoto, Atsushi Tsukamoto, Tetsuya Michiba, Ayano Kiriyama, Yuka Sakurai, Eiko Imaizumi, Kazuyoshi Saito, Kuniaki Fujita, Hiroshi Diagnostics (Basel) Article 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. MDPI 2022-12-16 /pmc/articles/PMC9777207/ /pubmed/36553202 http://dx.doi.org/10.3390/diagnostics12123195 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Teramoto, Atsushi
Tsukamoto, Tetsuya
Michiba, Ayano
Kiriyama, Yuka
Sakurai, Eiko
Imaizumi, Kazuyoshi
Saito, Kuniaki
Fujita, Hiroshi
Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
title Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
title_full Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
title_fullStr Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
title_full_unstemmed Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
title_short Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks
title_sort automated classification of idiopathic pulmonary fibrosis in pathological images using convolutional neural network and generative adversarial networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777207/
https://www.ncbi.nlm.nih.gov/pubmed/36553202
http://dx.doi.org/10.3390/diagnostics12123195
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