Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography.

Introduction: To date, most mammography-related AI models have been trained using either film or digital mammogram datasets with little overlap. We investigated whether or not combining film and digital mammography during training will help or hinder modern models designed for use on digital mammograms.

Methods: To this end, a total of six binary classifiers were trained for comparison.