Classification of round lesions in dual-energy ffdm using a convolutional neural network: Simulation study

Abstract

The presence of round cystic and solid mass lesions identified at mammogram screenings account for a large number of recalls. These recalls can cause undue patient anxiety and increased healthcare costs. Since cystic masses are nearly always benign, accurate classification of these lesions would be allow a significant reduction in recalls. This classification is very difficult using conventional mammogram screening data, but this study explores the possibility of performing the task on dual-energy full field digital mammography (FFDM) data. Since clinical data of this type is not readily available, realistic simulated data with different sources of variation are used. With this data, a deep convolutional neural network (CNN) was trained and evaluated. It achieved an AUC of 0.980 and 42% specificity at the 99% sensitivity level. These promising results should motivate further development of such imaging systems. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.