Computer-aided classification of lung nodules on CT images with expert knowledge

Chuangye Wan; Ling Ma; Xiabi Liu; Baowei Fei

doi:10.1117/12.2581888

Computer-aided classification of lung nodules on CT images with expert knowledge

Chuangye Wan
, Ling Ma^*
, Xiabi Liu
, Baowei Fei

^*此作品的通讯作者

计算机学院

Nankai University
University of Texas at Dallas
University of Texas Southwestern Medical Center

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

Accurate classification of pulmonary nodules in the CT images is critical for early detection of lung cancer as well as the assessment of the effect from COVID-19. In this paper, we propose a computer-aided classification method for lung nodules using expert knowledge. We use a decoupling metric learning model to describe the deep characteristics of the nodules and then calculate the similarity between the current nodule and the nodules in the database. By analyzing the returned nodules with the diagnosis information, we obtain the expert knowledge of similar nodules, based on which we make the decision of the current nodule. The proposed method has been evaluated on the benchmark LIDC-IDRI dataset and achieved an accuracy of 95.7% and AUC of 0.9901. The proposed classification method can have a variety of applications in lung cancer detection, diagnosis and therapy.

源语言	英语
主期刊名	Medical Imaging 2021
主期刊副标题	Image-Guided Procedures, Robotic Interventions, and Modeling
编辑	Cristian A. Linte, Jeffrey H. Siewerdsen
出版商	SPIE
ISBN（电子版）	9781510640252
DOI	https://doi.org/10.1117/12.2581888
出版状态	已出版 - 2021
活动	Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling - Virtual, Online 期限: 15 2月 2021 → 19 2月 2021

出版系列

姓名	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
卷	11598
ISSN（印刷版）	1605-7422

会议

会议	Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling
市	Virtual, Online
时期	15/02/21 → 19/02/21

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

10.1117/12.2581888

其它文件与链接

链接到 Scopus 的出版物

引用此

Wan, C., Ma, L., Liu, X., & Fei, B. (2021). Computer-aided classification of lung nodules on CT images with expert knowledge. 在 C. A. Linte, & J. H. Siewerdsen (编辑), Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling 文章 115982K (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; 卷 11598). SPIE. https://doi.org/10.1117/12.2581888

@inproceedings{160f07e9464242bfa9655cd3fa431099,

title = "Computer-aided classification of lung nodules on CT images with expert knowledge",

abstract = "Accurate classification of pulmonary nodules in the CT images is critical for early detection of lung cancer as well as the assessment of the effect from COVID-19. In this paper, we propose a computer-aided classification method for lung nodules using expert knowledge. We use a decoupling metric learning model to describe the deep characteristics of the nodules and then calculate the similarity between the current nodule and the nodules in the database. By analyzing the returned nodules with the diagnosis information, we obtain the expert knowledge of similar nodules, based on which we make the decision of the current nodule. The proposed method has been evaluated on the benchmark LIDC-IDRI dataset and achieved an accuracy of 95.7\% and AUC of 0.9901. The proposed classification method can have a variety of applications in lung cancer detection, diagnosis and therapy.",

keywords = "CT, Classification, Convolutional neural networks (CNN), Expert knowledge, Lung nodule",

author = "Chuangye Wan and Ling Ma and Xiabi Liu and Baowei Fei",

note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling ; Conference date: 15-02-2021 Through 19-02-2021",

year = "2021",

doi = "10.1117/12.2581888",

language = "English",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Linte, \{Cristian A.\} and Siewerdsen, \{Jeffrey H.\}",

booktitle = "Medical Imaging 2021",

address = "United States",

}

Wan, C, Ma, L, Liu, X & Fei, B 2021, Computer-aided classification of lung nodules on CT images with expert knowledge. 在 CA Linte & JH Siewerdsen (编辑), Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling., 115982K, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 卷 11598, SPIE, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, Virtual, Online, 15/02/21. https://doi.org/10.1117/12.2581888

Computer-aided classification of lung nodules on CT images with expert knowledge. / Wan, Chuangye; Ma, Ling; Liu, Xiabi 等.
Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling. 编辑 / Cristian A. Linte; Jeffrey H. Siewerdsen. SPIE, 2021. 115982K (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; 卷 11598).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Computer-aided classification of lung nodules on CT images with expert knowledge

AU - Wan, Chuangye

AU - Ma, Ling

AU - Liu, Xiabi

AU - Fei, Baowei

PY - 2021

Y1 - 2021

N2 - Accurate classification of pulmonary nodules in the CT images is critical for early detection of lung cancer as well as the assessment of the effect from COVID-19. In this paper, we propose a computer-aided classification method for lung nodules using expert knowledge. We use a decoupling metric learning model to describe the deep characteristics of the nodules and then calculate the similarity between the current nodule and the nodules in the database. By analyzing the returned nodules with the diagnosis information, we obtain the expert knowledge of similar nodules, based on which we make the decision of the current nodule. The proposed method has been evaluated on the benchmark LIDC-IDRI dataset and achieved an accuracy of 95.7% and AUC of 0.9901. The proposed classification method can have a variety of applications in lung cancer detection, diagnosis and therapy.

AB - Accurate classification of pulmonary nodules in the CT images is critical for early detection of lung cancer as well as the assessment of the effect from COVID-19. In this paper, we propose a computer-aided classification method for lung nodules using expert knowledge. We use a decoupling metric learning model to describe the deep characteristics of the nodules and then calculate the similarity between the current nodule and the nodules in the database. By analyzing the returned nodules with the diagnosis information, we obtain the expert knowledge of similar nodules, based on which we make the decision of the current nodule. The proposed method has been evaluated on the benchmark LIDC-IDRI dataset and achieved an accuracy of 95.7% and AUC of 0.9901. The proposed classification method can have a variety of applications in lung cancer detection, diagnosis and therapy.

KW - CT

KW - Classification

KW - Convolutional neural networks (CNN)

KW - Expert knowledge

KW - Lung nodule

UR - https://www.scopus.com/pages/publications/85105571556

U2 - 10.1117/12.2581888

DO - 10.1117/12.2581888

M3 - Conference contribution

AN - SCOPUS:85105571556

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2021

A2 - Linte, Cristian A.

A2 - Siewerdsen, Jeffrey H.

PB - SPIE

T2 - Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling

Y2 - 15 February 2021 through 19 February 2021

ER -

Computer-aided classification of lung nodules on CT images with expert knowledge

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

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引用此