Joint 2D-3D Breast Cancer Classification

Gongbo Liang; Xiaoqin Wang; Yu Zhang; Xin Xing; Hunter Blanton; Tawfiq Salem; Nathan Jacobs

doi:10.1109/BIBM47256.2019.8983048

Joint 2D-3D Breast Cancer Classification

Gongbo Liang, Xiaoqin Wang, Yu Zhang, Xin Xing, Hunter Blanton, Tawfiq Salem, Nathan Jacobs

Computer Science

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

33 Scopus citations

Abstract

Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Pages	692-696
Number of pages	5
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983048
State	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: Nov 18 2019 → Nov 21 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	11/18/19 → 11/21/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Digital mammography
clinical inspired
convolutional neural network
digital breast tomosynthesis

ASJC Scopus subject areas

Biochemistry
Biotechnology
Molecular Medicine
Modeling and Simulation
Health Informatics
Pharmacology (medical)
Public Health, Environmental and Occupational Health

UN SDGs

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

Access to Document

10.1109/BIBM47256.2019.8983048

Cite this

Liang, G., Wang, X., Zhang, Y., Xing, X., Blanton, H., Salem, T., & Jacobs, N. (2019). Joint 2D-3D Breast Cancer Classification. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 692-696). Article 8983048 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). https://doi.org/10.1109/BIBM47256.2019.8983048

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title = "Joint 2D-3D Breast Cancer Classification",

abstract = "Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.",

keywords = "Digital mammography, clinical inspired, convolutional neural network, digital breast tomosynthesis",

author = "Gongbo Liang and Xiaoqin Wang and Yu Zhang and Xin Xing and Hunter Blanton and Tawfiq Salem and Nathan Jacobs",

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doi = "10.1109/BIBM47256.2019.8983048",

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Liang, G, Wang, X, Zhang, Y, Xing, X, Blanton, H, Salem, T & Jacobs, N 2019, Joint 2D-3D Breast Cancer Classification. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983048, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, pp. 692-696, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 11/18/19. https://doi.org/10.1109/BIBM47256.2019.8983048

Joint 2D-3D Breast Cancer Classification. / Liang, Gongbo; Wang, Xiaoqin; Zhang, Yu et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. 2019. p. 692-696 8983048 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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

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AU - Wang, Xiaoqin

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AU - Jacobs, Nathan

PY - 2019/11

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N2 - Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.

AB - Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologists usually read both imaging modalities in combination; however, existing computer-aided diagnosis tools are designed using only one imaging modality. Inspired by clinical practice, we propose an innovative convolutional neural network (CNN) architecture for breast cancer classification, which uses both 2D and 3D mammograms, simultaneously. Our experiment shows that the proposed method significantly improves the performance of breast cancer classification. By assembling three CNN classifiers, the proposed model achieves 0.97 AUC, which is 34.72% higher than the methods using only one imaging modality.

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KW - clinical inspired

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Liang G, Wang X, Zhang Y, Xing X, Blanton H, Salem T et al. Joint 2D-3D Breast Cancer Classification. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. 2019. p. 692-696. 8983048. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983048

Joint 2D-3D Breast Cancer Classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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