Learning to combine decisions from multiple mammography views

Alan Joseph Bekker; Moron Shalhon; Hayit Greenspan; Jacob Goldberger

doi:10.1109/isbi.2015.7163825

Learning to combine decisions from multiple mammography views

Alan Joseph Bekker, Moron Shalhon, Hayit Greenspan, Jacob Goldberger

Tel Aviv University

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

2 Scopus citations

Abstract

In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single malignant or benign decision. The EM algorithm is used to find the parameters of the proposed model. Our method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness of optimally combining the decisions based on the two views.

Original language	English
Title of host publication	2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
Publisher	IEEE Computer Society
Pages	97-100
Number of pages	4
ISBN (Electronic)	9781479923748
DOIs	https://doi.org/10.1109/isbi.2015.7163825
State	Published - 21 Jul 2015
Event	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: 16 Apr 2015 → 19 Apr 2015

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2015-July
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/Territory	United States
City	Brooklyn
Period	16/04/15 → 19/04/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Computer-aided diagnosis
Mammography
Microcalcifications

Access to Document

10.1109/isbi.2015.7163825

Cite this

Bekker, A. J., Shalhon, M., Greenspan, H., & Goldberger, J. (2015). Learning to combine decisions from multiple mammography views. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 97-100). Article 7163825 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/isbi.2015.7163825

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title = "Learning to combine decisions from multiple mammography views",

abstract = "In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single malignant or benign decision. The EM algorithm is used to find the parameters of the proposed model. Our method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness of optimally combining the decisions based on the two views.",

keywords = "Computer-aided diagnosis, Mammography, Microcalcifications",

author = "Bekker, \{Alan Joseph\} and Moron Shalhon and Hayit Greenspan and Jacob Goldberger",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",

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doi = "10.1109/isbi.2015.7163825",

language = "אנגלית",

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Bekker, AJ, Shalhon, M, Greenspan, H & Goldberger, J 2015, Learning to combine decisions from multiple mammography views. in 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015., 7163825, Proceedings - International Symposium on Biomedical Imaging, vol. 2015-July, IEEE Computer Society, pp. 97-100, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16/04/15. https://doi.org/10.1109/isbi.2015.7163825

Learning to combine decisions from multiple mammography views. / Bekker, Alan Joseph; Shalhon, Moron; Greenspan, Hayit et al.
2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. IEEE Computer Society, 2015. p. 97-100 7163825 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July).

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

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AU - Goldberger, Jacob

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N2 - In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single malignant or benign decision. The EM algorithm is used to find the parameters of the proposed model. Our method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness of optimally combining the decisions based on the two views.

AB - In this paper we address the problem of differentiating between malignant and benign tumors based on their appearance in the CC and MLO mammography views. We describe a two-step classification method that is based on a view-level decision, implemented by a logistic regression classifier, followed by a stochastic combination of the two view-level indications into a single malignant or benign decision. The EM algorithm is used to find the parameters of the proposed model. Our method was evaluated on a standardized digital database for screening mammography (DDSM). Experimental results demonstrate the effectiveness of optimally combining the decisions based on the two views.

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KW - Microcalcifications

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ER -

Learning to combine decisions from multiple mammography views

Abstract

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Keywords

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