3D Inception U-net with Asymmetric Loss for Cancer Detection in Automated Breast Ultrasound

Abstract

Purpose: Breast cancer is the most common cancer and the leading cause of cancer-related deaths for women all over the world. Recently, automated breast ultrasound (ABUS) has become a new and promising screening modality for whole breast examination. However, reviewing volumetric ABUS is time-consuming and lesions could be missed during the examination. Therefore, computer-aided cancer detection in ABUS volume is extremely expected to help clinician for the breast cancer screening. Methods: We develop a novel end-to-end 3D convolutional network for automated cancer detection in ABUS volume, in order to accelerate reviewing and meanwhile to provide high detection sensitivity with low false positives (FPs). Specifically, an efficient 3D Inception Unet-style architecture with fusion deep supervision mechanism is proposed to attain decent detection performance. In addition, a novel asymmetric loss is designed to help the network balancing false positive and false negative regions, thus improving detection sensitivity for small cancerous lesions. Results: The efficacy of our network was extensively validated on a dataset including 196 patients with 661 cancer regions. Our network obtained a detection sensitivity of 95.1% with 3.0 FPs per ABUS volume. Furthermore, the average inference time of the network was 0.1 second per volume, which largely shortens the conventional reviewing time. Conclusions: The proposed network provides efficient and accurate cancer detection scheme using ABUS volume, and may assist clinicians for more efficient breast cancer screening.