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

^*Corresponding author for this work

School of Computer Science and Technology

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

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

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Medical Imaging 2021
Subtitle of host publication	Image-Guided Procedures, Robotic Interventions, and Modeling
Editors	Cristian A. Linte, Jeffrey H. Siewerdsen
Publisher	SPIE
ISBN (Electronic)	9781510640252
DOIs	https://doi.org/10.1117/12.2581888
Publication status	Published - 2021
Event	Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling - Virtual, Online Duration: 15 Feb 2021 → 19 Feb 2021

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	11598
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling
City	Virtual, Online
Period	15/02/21 → 19/02/21

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

CT
Classification
Convolutional neural networks (CNN)
Expert knowledge
Lung nodule

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10.1117/12.2581888

Cite this

Wan, C., Ma, L., Liu, X., & Fei, B. (2021). Computer-aided classification of lung nodules on CT images with expert knowledge. In C. A. Linte, & J. H. Siewerdsen (Eds.), Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling Article 115982K (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 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. in CA Linte & JH Siewerdsen (eds), Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling., 115982K, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 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 et al.
Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling. ed. / Cristian A. Linte; Jeffrey H. Siewerdsen. SPIE, 2021. 115982K (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11598).

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

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

Abstract

Publication series

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UN SDGs

Keywords

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