@inproceedings{c0ae52ec26774bc1ba55af0b893e710f,
title = "Automatic breast density classification using a convolutional neural network architecture search procedure",
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.",
keywords = "Mammograms, automatic assessment, breast density, convolutional neural networks, feature learning",
author = "Pablo Fonseca and Julio Mendoza and Jacques Wainer and Jose Ferrer and Joseph Pinto and Jorge Guerrero and Benjamin Castaneda",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; SPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis ; Conference date: 22-02-2015 Through 25-02-2015",
year = "2015",
doi = "10.1117/12.2081576",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Hadjiiski, {Lubomir M.} and Hadjiiski, {Lubomir M.} and Tourassi, {Georgia D.} and Tourassi, {Georgia D.}",
booktitle = "Medical Imaging 2015",
}
