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Proceedings Paper

Automatic breast density classification using a convolutional neural network architecture search procedure
Author(s): Pablo Fonseca; Julio Mendoza; Jacques Wainer; Jose Ferrer; Joseph Pinto; Jorge Guerrero; Benjamin Castaneda
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Paper Abstract

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists’ classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941428 (20 March 2015); doi: 10.1117/12.2081576
Show Author Affiliations
Pablo Fonseca, Univ. Estadual de Campinas (Brazil)
Julio Mendoza, Univ. Estadual de Campinas (Brazil)
Jacques Wainer, Univ. Estadual de Campinas (Brazil)
Jose Ferrer, Medical Innovation and Technology S.A.C. (Peru)
Joseph Pinto, Oncosalud (Peru)
Jorge Guerrero, Oncosalud (Peru)
Benjamin Castaneda, Pontificia Univ. Católica del Perú (Peru)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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