In this paper, we employ a deep Convolutional Neural Network CNN for the classification of regions of interest of malignant soft tissue lesions in mammography and show that it performs on par to experienced radiologists. The CNN was applied to 398 regions of 5$$\,\times \,$$5i¾?cm, half of which contained a malignant lesion and the other half depicted suspicious regions in normal mammograms detected by a traditional CAD system. Four radiologists participated in the study. ROC analysis was used for evaluating results. The AUC of CNN was 0.87, which was higher than the mean AUC of the radiologists 0.84, though the difference was not significant.
['Thijs Kooi', 'Albert Gubern-Mérida', 'Jan-Jurre Mordang', 'Ritse M. Mann', 'Ruud M. Pijnappel', 'Klaas Schuur', 'Ard den Heeten', 'Nico Karssemeijer']
A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography