Deep feature learning for pulmonary nodule classification in a lung CT

Bum Chae Kim; Yu Sub Sung; Heung Il Suk

doi:10.1109/IWW-BCI.2016.7457462

Deep feature learning for pulmonary nodule classification in a lung CT

Bum Chae Kim, Yu Sub Sung, Heung Il Suk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

47 Citations (Scopus)

Abstract

In this paper, we propose a novel method of identifying pulmonary nodules in a lung CT. Specifically, we devise a deep neural network by which we extract abstract information inherent in raw hand-crafted imaging features. We then combine the deep learned representations with the original raw imaging features into a long feature vector. By taking the combined feature vectors, we train a classifier, preceded by a feature selection via t-test. To validate the effectiveness of the proposed method, we performed experiments on our in-house dataset of 20 subjects; 3,598 pulmonary nodules (malignant: 178, benign: 3,420), which were manually segmented by a radiologist. In our experiments, we achieved the maximal accuracy of 95.5%, sensitivity of 94.4%, and AUC of 0.987, outperforming the competing method.

Original language	English
Title of host publication	4th International Winter Conference on Brain-Computer Interface, BCI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467378413
DOIs	https://doi.org/10.1109/IWW-BCI.2016.7457462
Publication status	Published - 2016 Apr 20
Event	4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of Duration: 2016 Feb 22 → 2016 Feb 24

Publication series

Name	4th International Winter Conference on Brain-Computer Interface, BCI 2016

Other

Other	4th International Winter Conference on Brain-Computer Interface, BCI 2016
Country/Territory	Korea, Republic of
City	Gangwon Province
Period	16/2/22 → 16/2/24

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1C1A1A01052216). The authors gratefully acknowledge technical supports from Biomedical Imaging Infrastructure, Department of Radiology, Asan Medical Center.

Publisher Copyright:
© 2016 IEEE.

Keywords

Deep learning
Lung cancer
Pulmonary nodule classification
Stacked denoising autoencoder

ASJC Scopus subject areas

Human-Computer Interaction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IWW-BCI.2016.7457462

Cite this

Kim, B. C., Sung, Y. S., & Suk, H. I. (2016). Deep feature learning for pulmonary nodule classification in a lung CT. In 4th International Winter Conference on Brain-Computer Interface, BCI 2016 Article 7457462 (4th International Winter Conference on Brain-Computer Interface, BCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2016.7457462

Deep feature learning for pulmonary nodule classification in a lung CT. / Kim, Bum Chae; Sung, Yu Sub; Suk, Heung Il.
4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7457462 (4th International Winter Conference on Brain-Computer Interface, BCI 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, BC, Sung, YS & Suk, HI 2016, Deep feature learning for pulmonary nodule classification in a lung CT. in 4th International Winter Conference on Brain-Computer Interface, BCI 2016., 7457462, 4th International Winter Conference on Brain-Computer Interface, BCI 2016, Institute of Electrical and Electronics Engineers Inc., 4th International Winter Conference on Brain-Computer Interface, BCI 2016, Gangwon Province, Korea, Republic of, 16/2/22. https://doi.org/10.1109/IWW-BCI.2016.7457462

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title = "Deep feature learning for pulmonary nodule classification in a lung CT",

abstract = "In this paper, we propose a novel method of identifying pulmonary nodules in a lung CT. Specifically, we devise a deep neural network by which we extract abstract information inherent in raw hand-crafted imaging features. We then combine the deep learned representations with the original raw imaging features into a long feature vector. By taking the combined feature vectors, we train a classifier, preceded by a feature selection via t-test. To validate the effectiveness of the proposed method, we performed experiments on our in-house dataset of 20 subjects; 3,598 pulmonary nodules (malignant: 178, benign: 3,420), which were manually segmented by a radiologist. In our experiments, we achieved the maximal accuracy of 95.5%, sensitivity of 94.4%, and AUC of 0.987, outperforming the competing method.",

keywords = "Deep learning, Lung cancer, Pulmonary nodule classification, Stacked denoising autoencoder",

author = "Kim, {Bum Chae} and Sung, {Yu Sub} and Suk, {Heung Il}",

note = "Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1C1A1A01052216). The authors gratefully acknowledge technical supports from Biomedical Imaging Infrastructure, Department of Radiology, Asan Medical Center. Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th International Winter Conference on Brain-Computer Interface, BCI 2016 ; Conference date: 22-02-2016 Through 24-02-2016",

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N2 - In this paper, we propose a novel method of identifying pulmonary nodules in a lung CT. Specifically, we devise a deep neural network by which we extract abstract information inherent in raw hand-crafted imaging features. We then combine the deep learned representations with the original raw imaging features into a long feature vector. By taking the combined feature vectors, we train a classifier, preceded by a feature selection via t-test. To validate the effectiveness of the proposed method, we performed experiments on our in-house dataset of 20 subjects; 3,598 pulmonary nodules (malignant: 178, benign: 3,420), which were manually segmented by a radiologist. In our experiments, we achieved the maximal accuracy of 95.5%, sensitivity of 94.4%, and AUC of 0.987, outperforming the competing method.

AB - In this paper, we propose a novel method of identifying pulmonary nodules in a lung CT. Specifically, we devise a deep neural network by which we extract abstract information inherent in raw hand-crafted imaging features. We then combine the deep learned representations with the original raw imaging features into a long feature vector. By taking the combined feature vectors, we train a classifier, preceded by a feature selection via t-test. To validate the effectiveness of the proposed method, we performed experiments on our in-house dataset of 20 subjects; 3,598 pulmonary nodules (malignant: 178, benign: 3,420), which were manually segmented by a radiologist. In our experiments, we achieved the maximal accuracy of 95.5%, sensitivity of 94.4%, and AUC of 0.987, outperforming the competing method.

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Deep feature learning for pulmonary nodule classification in a lung CT

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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