Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification
Computer-aided diagnosis systems with deep learning frameworks have been used to identify benign and malignant pulmonary nodules in lung cancer diagnosis. It's commonly known that a premise of training complex deep neural nets is the large-scale labeled datasets. However, the abundance of label...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9066829/ |
| _version_ | 1818619759473721344 |
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| author | Yan Kuang Tian Lan Xueqiao Peng Gati Elvis Selasi Qiao Liu Junyi Zhang |
| author_facet | Yan Kuang Tian Lan Xueqiao Peng Gati Elvis Selasi Qiao Liu Junyi Zhang |
| author_sort | Yan Kuang |
| collection | DOAJ |
| description | Computer-aided diagnosis systems with deep learning frameworks have been used to identify benign and malignant pulmonary nodules in lung cancer diagnosis. It's commonly known that a premise of training complex deep neural nets is the large-scale labeled datasets. However, the abundance of labeled datasets is usually unavailable in many medical image domains. This factor can lead to the poor generalization performance of deep learning models. In this paper, we propose a novel multi-discriminator generative adversarial network model combined with an encoder for the classification of benign and malignant pulmonary nodules. To the best of our knowledge, we are the first to apply unsupervised learning to identify benign and malignant lung nodules. Firstly, we use a multi-discriminator generative adversarial network to build a generative model trained with unlabeled benign lung nodule images. Then an encoder is combined with the trained generative model to establish a mapping of benign pulmonary nodule images to the latent space. The benign and malignant lung nodules are scored by calculating the GAN discriminator feature loss and image reconstruction loss. The model yields high anomaly scores on malignant images and low anomaly scores on benign images. Experimental results show that our method with only a small number of unlabeled datasets could achieve more competitive results compared with other supervised deep learning approaches. |
| first_indexed | 2024-12-16T17:42:35Z |
| format | Article |
| id | doaj.art-ed95e0493c9b45098f8d4139dd1c8e5a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:42:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ed95e0493c9b45098f8d4139dd1c8e5a2022-12-21T22:22:33ZengIEEEIEEE Access2169-35362020-01-018777257773410.1109/ACCESS.2020.29879619066829Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy ClassificationYan Kuang0https://orcid.org/0000-0002-4080-2839Tian Lan1https://orcid.org/0000-0001-6381-7657Xueqiao Peng2https://orcid.org/0000-0001-8004-393XGati Elvis Selasi3https://orcid.org/0000-0002-6724-3424Qiao Liu4https://orcid.org/0000-0002-2573-9544Junyi Zhang5https://orcid.org/0000-0002-2392-1967School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaThe 54th Research Institute of CETC, Shijiazhuang, ChinaComputer-aided diagnosis systems with deep learning frameworks have been used to identify benign and malignant pulmonary nodules in lung cancer diagnosis. It's commonly known that a premise of training complex deep neural nets is the large-scale labeled datasets. However, the abundance of labeled datasets is usually unavailable in many medical image domains. This factor can lead to the poor generalization performance of deep learning models. In this paper, we propose a novel multi-discriminator generative adversarial network model combined with an encoder for the classification of benign and malignant pulmonary nodules. To the best of our knowledge, we are the first to apply unsupervised learning to identify benign and malignant lung nodules. Firstly, we use a multi-discriminator generative adversarial network to build a generative model trained with unlabeled benign lung nodule images. Then an encoder is combined with the trained generative model to establish a mapping of benign pulmonary nodule images to the latent space. The benign and malignant lung nodules are scored by calculating the GAN discriminator feature loss and image reconstruction loss. The model yields high anomaly scores on malignant images and low anomaly scores on benign images. Experimental results show that our method with only a small number of unlabeled datasets could achieve more competitive results compared with other supervised deep learning approaches.https://ieeexplore.ieee.org/document/9066829/Computer-aided diagnosis (CAD)lung nodulemalignancy classificationunsupervised learninggenerative adversarial networks |
| spellingShingle | Yan Kuang Tian Lan Xueqiao Peng Gati Elvis Selasi Qiao Liu Junyi Zhang Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification IEEE Access Computer-aided diagnosis (CAD) lung nodule malignancy classification unsupervised learning generative adversarial networks |
| title | Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification |
| title_full | Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification |
| title_fullStr | Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification |
| title_full_unstemmed | Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification |
| title_short | Unsupervised Multi-Discriminator Generative Adversarial Network for Lung Nodule Malignancy Classification |
| title_sort | unsupervised multi discriminator generative adversarial network for lung nodule malignancy classification |
| topic | Computer-aided diagnosis (CAD) lung nodule malignancy classification unsupervised learning generative adversarial networks |
| url | https://ieeexplore.ieee.org/document/9066829/ |
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