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

被引：35

作者：

Fonseca, Pablo ^{[1
]}

Mendoza, Julio ^{[1
]}

Wainer, Jacques ^{[1
]}

Ferrer, Jose ^{[2
]}

Pinto, Joseph ^{[3
]}

Guerrero, Jorge ^{[3
]}

Castaneda, Benjamin ^{[4
]}

机构：

[1] Univ Estadual Campinas, RECOD Lab, Campinas, SP, Brazil

[2] Med Innovat & Technol, Res & Dev, Lima, Peru

[3] Oncosalud, Dept Radiol, Lima, Peru

[4] Pontifical Catholic Univ Peru, Lab Imagenes Med, Lima, Peru

来源：

MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS | 2015年 / 9414卷

关键词：

Mammograms; breast density; automatic assessment; feature learning; convolutional neural networks;

D O I：

10.1117/12.2081576

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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.

引用

页数：8

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