Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning

Mor Yemini; Yaniv Zigel; Dror Lederman

doi:10.1109/ICSEE.2018.8646252

Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning

Mor Yemini
, Yaniv Zigel
, Dror Lederman

Department of Biomedical Engineering

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

23 Scopus citations

Abstract

This paper addresses the problem of mass detection in mammograms. It has long ago been shown that computer-Aided diagnosis (CAD) schemes have the potential of improving breast cancer diagnosis performance. We propose a CAD scheme based on convolutional neural networks, using transfer representation learning and the Google Inception-V3 architecture. Artificially generated mammograms and data augmentation techniques are used to expand and balance the available database at train time. The performance of the proposed scheme is evaluated based on the receiver operating characteristics (ROC) curve. Areas under the ROC curve of 0.78 and 0.86 were obtained using artificially-generated mammograms and augmentation, respectively.

Original language	English
Title of host publication	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9781538663783
DOIs	https://doi.org/10.1109/ICSEE.2018.8646252
State	Published - 2 Jul 2018
Event	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 - Eilat, Israel Duration: 12 Dec 2018 → 14 Dec 2018

Publication series

Name	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

Conference

Conference	2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018
Country/Territory	Israel
City	Eilat
Period	12/12/18 → 14/12/18

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Breast cancer
convolutional neural networks
deep learning
mammogram
transfer learning

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ICSEE.2018.8646252

Cite this

Yemini, M., Zigel, Y., & Lederman, D. (2018). Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning. In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 Article 8646252 (2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICSEE.2018.8646252

Yemini, Mor ; Zigel, Yaniv ; Lederman, Dror. / Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning. 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018. Institute of Electrical and Electronics Engineers, 2018. (2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018).

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title = "Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning",

abstract = "This paper addresses the problem of mass detection in mammograms. It has long ago been shown that computer-Aided diagnosis (CAD) schemes have the potential of improving breast cancer diagnosis performance. We propose a CAD scheme based on convolutional neural networks, using transfer representation learning and the Google Inception-V3 architecture. Artificially generated mammograms and data augmentation techniques are used to expand and balance the available database at train time. The performance of the proposed scheme is evaluated based on the receiver operating characteristics (ROC) curve. Areas under the ROC curve of 0.78 and 0.86 were obtained using artificially-generated mammograms and augmentation, respectively.",

keywords = "Breast cancer, convolutional neural networks, deep learning, mammogram, transfer learning",

author = "Mor Yemini and Yaniv Zigel and Dror Lederman",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 ; Conference date: 12-12-2018 Through 14-12-2018",

year = "2018",

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doi = "10.1109/ICSEE.2018.8646252",

language = "English",

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Yemini, M, Zigel, Y & Lederman, D 2018, Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning. in 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018., 8646252, 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018, Institute of Electrical and Electronics Engineers, 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018, Eilat, Israel, 12/12/18. https://doi.org/10.1109/ICSEE.2018.8646252

Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning. / Yemini, Mor; Zigel, Yaniv; Lederman, Dror.
2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018. Institute of Electrical and Electronics Engineers, 2018. 8646252 (2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018).

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

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N2 - This paper addresses the problem of mass detection in mammograms. It has long ago been shown that computer-Aided diagnosis (CAD) schemes have the potential of improving breast cancer diagnosis performance. We propose a CAD scheme based on convolutional neural networks, using transfer representation learning and the Google Inception-V3 architecture. Artificially generated mammograms and data augmentation techniques are used to expand and balance the available database at train time. The performance of the proposed scheme is evaluated based on the receiver operating characteristics (ROC) curve. Areas under the ROC curve of 0.78 and 0.86 were obtained using artificially-generated mammograms and augmentation, respectively.

AB - This paper addresses the problem of mass detection in mammograms. It has long ago been shown that computer-Aided diagnosis (CAD) schemes have the potential of improving breast cancer diagnosis performance. We propose a CAD scheme based on convolutional neural networks, using transfer representation learning and the Google Inception-V3 architecture. Artificially generated mammograms and data augmentation techniques are used to expand and balance the available database at train time. The performance of the proposed scheme is evaluated based on the receiver operating characteristics (ROC) curve. Areas under the ROC curve of 0.78 and 0.86 were obtained using artificially-generated mammograms and augmentation, respectively.

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KW - convolutional neural networks

KW - deep learning

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KW - transfer learning

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U2 - 10.1109/ICSEE.2018.8646252

DO - 10.1109/ICSEE.2018.8646252

M3 - Conference contribution

AN - SCOPUS:85063165247

T3 - 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

BT - 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018

PB - Institute of Electrical and Electronics Engineers

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Y2 - 12 December 2018 through 14 December 2018

ER -

Yemini M, Zigel Y, Lederman D. Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning. In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018. Institute of Electrical and Electronics Engineers. 2018. 8646252. (2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018). doi: 10.1109/ICSEE.2018.8646252

Detecting Masses in Mammograms using Convolutional Neural Networks and Transfer Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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