The purpose of this study is to assess the feasibility of utilizing deep learning to aid radiologists with the interpretation of lesions discovered in breast ultrasound (BUS) images during routine clinical screenings. Based on a review of the literature on medical imaging and computer-assisted detection (CAD) systems for BUS interpretation, a multitask learning model using a pre-trained state-of-the-art convolutional neural network (CNN) was developed and trained using various image augmentation techniques known to increase performance. The research found that the best model identified from this study performed on par with that of a trained radiologist in its ability to predict lesion pathology. However, no definitive conclusions could be drawn about the model’s multitask performance due in part to the limited data available.