Classification and Model Interpretation of Mammography Positioned Quality for Posterior Breast

According to the criteria revealed by the PGMI evaluation system, fat visualized posterior to glandular tissue is a significant criterion for mammography positioned quality evaluation. This paper introduces the quality evaluation method in craniocaudal-view mammograms. The proposed method implements a classifier with a convolutional neural network (CNN) and then analyzes the inner and outer regions of the posterior breast. To find the proper CNN model, we experiment with the classification with five CNNs, including, Squeezenet, Densenet-121, Alexnet, Vgg-19, and Resnet-101. Furthermore, analyzing the gradient-weighted class activation mapping extracted from CNN models interprets what features the model observes. The experiment results demonstrate that Densenet-121 is the best choice for positioned quality evaluation of posterior breasts.