Whole mammogram image classification with convolutional neural networks

Xiaofei Zhang; Yi Zhang; Erik Y. Han; Nathan Jacobs; Qiong Han; Xiaoqin Wang; Jinze Liu

doi:10.1109/BIBM.2017.8217738

Whole mammogram image classification with convolutional neural networks

Xiaofei Zhang, Yi Zhang, Erik Y. Han, Nathan Jacobs, Qiong Han, Xiaoqin Wang, Jinze Liu

Computer Science

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

39 Scopus citations

Abstract

Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, we had obtained over 3000 high-quality original mammograms with approval from an institutional review board at the University of Kentucky. Different classifiers based on CNNs were built, and each classifier was evaluated based on its performance relative to truth values generated by histology results from biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our results showed that CNN model we had built and optimized via data augmentation and transfer learning have a great potential for automatic breast cancer detection using mammograms.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Editors	Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
Pages	700-704
Number of pages	5
ISBN (Electronic)	9781509030491
DOIs	https://doi.org/10.1109/BIBM.2017.8217738
State	Published - Dec 15 2017
Event	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: Nov 13 2017 → Nov 16 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume	2017-January

Conference

Conference	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/Territory	United States
City	Kansas City
Period	11/13/17 → 11/16/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Mammogram
classification
convolutional neural network

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

UN SDGs

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

Access to Document

10.1109/BIBM.2017.8217738

Cite this

Zhang, X., Zhang, Y., Han, E. Y., Jacobs, N., Han, Q., Wang, X., & Liu, J. (2017). Whole mammogram image classification with convolutional neural networks. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C.-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 700-704). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). https://doi.org/10.1109/BIBM.2017.8217738

Zhang, Xiaofei ; Zhang, Yi ; Han, Erik Y. et al. / Whole mammogram image classification with convolutional neural networks. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. 2017. pp. 700-704 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).

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title = "Whole mammogram image classification with convolutional neural networks",

abstract = "Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, we had obtained over 3000 high-quality original mammograms with approval from an institutional review board at the University of Kentucky. Different classifiers based on CNNs were built, and each classifier was evaluated based on its performance relative to truth values generated by histology results from biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our results showed that CNN model we had built and optimized via data augmentation and transfer learning have a great potential for automatic breast cancer detection using mammograms.",

keywords = "Mammogram, classification, convolutional neural network",

author = "Xiaofei Zhang and Yi Zhang and Han, {Erik Y.} and Nathan Jacobs and Qiong Han and Xiaoqin Wang and Jinze Liu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",

year = "2017",

month = dec,

day = "15",

doi = "10.1109/BIBM.2017.8217738",

language = "English",

series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

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Zhang, X, Zhang, Y, Han, EY, Jacobs, N, Han, Q, Wang, X & Liu, J 2017, Whole mammogram image classification with convolutional neural networks. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, pp. 700-704, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217738

Whole mammogram image classification with convolutional neural networks. / Zhang, Xiaofei; Zhang, Yi; Han, Erik Y. et al.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. 2017. p. 700-704 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

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

TY - GEN

T1 - Whole mammogram image classification with convolutional neural networks

AU - Zhang, Xiaofei

AU - Zhang, Yi

AU - Han, Erik Y.

AU - Jacobs, Nathan

AU - Han, Qiong

AU - Wang, Xiaoqin

AU - Liu, Jinze

PY - 2017/12/15

Y1 - 2017/12/15

N2 - Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, we had obtained over 3000 high-quality original mammograms with approval from an institutional review board at the University of Kentucky. Different classifiers based on CNNs were built, and each classifier was evaluated based on its performance relative to truth values generated by histology results from biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our results showed that CNN model we had built and optimized via data augmentation and transfer learning have a great potential for automatic breast cancer detection using mammograms.

AB - Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This technique has achieved significant advancements in large-set image-classification challenges in recent years. In this study, we had obtained over 3000 high-quality original mammograms with approval from an institutional review board at the University of Kentucky. Different classifiers based on CNNs were built, and each classifier was evaluated based on its performance relative to truth values generated by histology results from biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our results showed that CNN model we had built and optimized via data augmentation and transfer learning have a great potential for automatic breast cancer detection using mammograms.

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M3 - Conference contribution

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BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

A2 - Yoo, Illhoi

A2 - Zheng, Jane Huiru

A2 - Gong, Yang

A2 - Hu, Xiaohua Tony

A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

A2 - Gao, Jean

A2 - Korkin, Dmitry

T2 - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

Y2 - 13 November 2017 through 16 November 2017

ER -

Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X et al. Whole mammogram image classification with convolutional neural networks. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu CR, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. 2017. p. 700-704. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). doi: 10.1109/BIBM.2017.8217738

Whole mammogram image classification with convolutional neural networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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