Computer-aided Tumor Diagnosis in Automated Breast Ultrasound using 3D Detection Network

TL;DR

This paper introduces a novel 3D detection and classification network for automated breast ultrasound that improves tumor localization and benign/malignant differentiation, achieving high sensitivity and accuracy on a patient dataset.

Contribution

A new two-stage 3D detection network utilizing a similarity loss and IoU-balanced classification loss for better tumor detection and classification in ABUS images.

Findings

Achieved 97.66% sensitivity in tumor detection.

Attained an AUC of 0.8720 for classification.

Reduced false positives to 1.23 per case.

Abstract

Automated breast ultrasound (ABUS) is a new and promising imaging modality for breast cancer detection and diagnosis, which could provide intuitive 3D information and coronal plane information with great diagnostic value. However, manually screening and diagnosing tumors from ABUS images is very time-consuming and overlooks of abnormalities may happen. In this study, we propose a novel two-stage 3D detection network for locating suspected lesion areas and further classifying lesions as benign or malignant tumors. Specifically, we propose a 3D detection network rather than frequently-used segmentation network to locate lesions in ABUS images, thus our network can make full use of the spatial context information in ABUS images. A novel similarity loss is designed to effectively distinguish lesions from background. Then a classification network is employed to identify the located lesions as benign or malignant. An IoU-balanced classification loss is adopted to improve the correlation between classification and localization task. The efficacy of our network is verified from a collected dataset of 418 patients with 145 benign tumors and 273 malignant tumors. Experiments show our network attains a sensitivity of 97.66% with 1.23 false positives (FPs), and has an area under the curve(AUC) value of 0.8720.