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A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD
PURPOSE: Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6500711/ https://www.ncbi.nlm.nih.gov/pubmed/31119180 http://dx.doi.org/10.1155/2019/6425963 |
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author | Zheng, Guangyuan Han, Guanghui Soomro, Nouman Q. Ma, Linjuan Zhang, Fuquan Zhao, Yanfeng Zhao, Xinming Zhou, Chunwu |
author_facet | Zheng, Guangyuan Han, Guanghui Soomro, Nouman Q. Ma, Linjuan Zhang, Fuquan Zhao, Yanfeng Zhao, Xinming Zhou, Chunwu |
author_sort | Zheng, Guangyuan |
collection | PubMed |
description | PURPOSE: Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD. METHODS: From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation. RESULTS: Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively. CONCLUSION: The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis. |
format | Online Article Text |
id | pubmed-6500711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-65007112019-05-22 A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD Zheng, Guangyuan Han, Guanghui Soomro, Nouman Q. Ma, Linjuan Zhang, Fuquan Zhao, Yanfeng Zhao, Xinming Zhou, Chunwu Biomed Res Int Research Article PURPOSE: Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD. METHODS: From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation. RESULTS: Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively. CONCLUSION: The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis. Hindawi 2019-04-15 /pmc/articles/PMC6500711/ /pubmed/31119180 http://dx.doi.org/10.1155/2019/6425963 Text en Copyright © 2019 Guangyuan Zheng et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zheng, Guangyuan Han, Guanghui Soomro, Nouman Q. Ma, Linjuan Zhang, Fuquan Zhao, Yanfeng Zhao, Xinming Zhou, Chunwu A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD |
title | A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD |
title_full | A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD |
title_fullStr | A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD |
title_full_unstemmed | A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD |
title_short | A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD |
title_sort | novel computer-aided diagnosis scheme on small annotated set: g2c-cad |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6500711/ https://www.ncbi.nlm.nih.gov/pubmed/31119180 http://dx.doi.org/10.1155/2019/6425963 |
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