Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network.

Background And Objectives: Automated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this study, an effective and fast computer-aided detection (CADe) system based on a 3-D convolutional neural network (CNN) is proposed as the second reader for the physician in order to decrease the reviewing time and misdetection rate.

Methods: Our CADe system uses the sliding window method, a CNN-based determining model, and a candidate aggregation algorithm.