Automatic breast density classification using a convolutional neural network architecture search procedure

Pablo Fonseca; Julio Mendoza; Jacques Wainer; Jose Ferrer; Joseph Pinto; Jorge Guerrero; Benjamin Castaneda

doi:10.1117/12.2081576

20 March 2015 Automatic breast density classification using a convolutional neural network architecture search procedure

Pablo Fonseca, Julio Mendoza, Jacques Wainer, Jose Ferrer, Joseph Pinto, Jorge Guerrero, Benjamin Castaneda

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 941428 (2015) https://doi.org/10.1117/12.2081576
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

Pablo Fonseca, Julio Mendoza, Jacques Wainer, Jose Ferrer, Joseph Pinto, Jorge Guerrero, and Benjamin Castaneda "Automatic breast density classification using a convolutional neural network architecture search procedure", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941428 (20 March 2015); https://doi.org/10.1117/12.2081576

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