BI-RADS Features-Based Computer-Aided Diagnosis of Abnormalities in Mammographic Images

Saejoon Kim, Sejong Yoon
Published 2007 in
2007 6th International Special Topic Conference…

Abstract

In the analysis of digital or digitized mammographic images, a requirement is to learn to separate benign abnormalities from malignant ones. Such an activity could form part of a computer-aided diagnosis (CAD) tool. We present a CAD study of mass and calcification lesions found in digital database of screening mammography (DDSM) using BI-RADS-based features… (More)

Topics

9 Figures and Tables

Cited By

Using BI-RADS Descriptors and Ensemble Learning for Classifying Masses in Mammograms

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2009

New mass description in mammographies

I. Cheikhrouhou, K. Djemal, D. Sellami, H. Maaref, N. Derbel
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References

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Computerized Classification Can Reduce Unnecessary Biopsies in BI-RADS Category 4A Lesions

Isaac Leichter, Richard Lederman, Shalom Buchbinder, Yossi Srour, Philippe Bamberger, Fanny Sperber
Digital Mammography / IWDM
2006

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Applying support vector machines to breast cancer diagnosis using screen film mammogram data

Walker H. Land, Lut Wong, Daniel W. McKee, Mark J. Embrechts, Rizly Salih, Frances R. Anderson
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A support vector machine approach for detection of microcalcifications

Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Nikolas P. Galatsanos, Robert M. Nishikawa
IEEE Transactions on Medical Imaging
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Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications.

Y Jiang, R M Nishikawa, R A Schmidt, A Y Toledano, K Doi
Radiology
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BI-RADS categorization as a predictor of malignancy.

S G Orel, N Kay, C Reynolds, D C Sullivan
Radiology
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M. Heath
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BI-RADS Features-Based Computer-Aided Diagnosis of Abnormalities in Mammographic Images

Abstract

Topics

9 Figures and Tables

Cited By

Using BI-RADS Descriptors and Ensemble Learning for Classifying Masses in Mammograms

New mass description in mammographies

References

Computerized Classification Can Reduce Unnecessary Biopsies in BI-RADS Category 4A Lesions

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications,

Applying support vector machines to breast cancer diagnosis using screen film mammogram data

A support vector machine approach for detection of microcalcifications

Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications.

BI-RADS categorization as a predictor of malignancy.

Current status of the digital database of screening mammography

Slides referencing similar topics

BI-RADS Features-Based Computer-Aided Diagnosis of Abnormalities in Mammographic Images

Abstract

Topics

9 Figures and Tables

Cited By

Using BI-RADS Descriptors and Ensemble Learning for Classifying Masses in Mammograms

New mass description in mammographies

References

Computerized Classification Can Reduce Unnecessary Biopsies in BI-RADS Category 4A Lesions

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications,

Applying support vector machines to breast cancer diagnosis using screen film mammogram data

A support vector machine approach for detection of microcalcifications

Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications.

BI-RADS categorization as a predictor of malignancy.

Current status of the digital database of screening mammography

Similar Papers

Slides referencing similar topics