Sonography is an important tool in the detection and characterization of breast masses. Though consistently shown to detect additional cancers as a supplemental imaging modality, breast ultrasound has been noted to have a high false-positive rate relative to mammography and magnetic resonance imaging. Here, we propose a deep neural network that can detect benign and malignant lesions in breast ultrasound images. The network achieves an area under the receiver operating characteristic curve (AUROC) of 0.902 (95\% CI: 0.892-0.911) on a test set consisting of 103,611 exams (around 2 million images) collected at Anonymized Institution between 2012 and 2019. To confirm its generalizability, we evaluated the network on an independent external test set on which it achieved an AUROC of 0.908 (95\% CI: 0.884 - 0.933). This highlights the potential of AI in improving accuracy, consistency, and efficiency of breast ultrasound diagnostics worldwide.