Domain specific convolutional neural nets for detection of architectural distortion in mammograms

Abstract

Detection of Architectural distortion (AD) is important for ruling out possible pre-malignant lesions in breast, but due to its subtlety, it is often missed on the screening mammograms. In this work we suggest a novel AD detection method based on region proposal convolution neural nets (R-CNN). When the data is scarce, as typically the case in medical domain, R-CNN yields poor results. In this study, we suggest a new R-CNN method addressing this shortcoming by using a pretrained network on a candidate region guided by clinical observations. We test our method on the publicly available DDSM data set, with comparison to the latest faster R-CNN and previous works. Our detection accuracy allows binary image classification (normal vs. containing AD) with over 80% sensitivity and specificity, and yields 0.46 false-positives per image at 83% true-positive rate, for localization accuracy. These measures significantly improve the best results in the literature.