Automated assessment of BI-RADS categories for ultrasound images using multi-scale neural networks with an order-constrained loss function.

Ultrasound imaging is one of the most frequently used diagnostic tools for detecting and analyzing abnormalities of the breast. Recently proposed methods for the automated analysis of breast ultrasound images have shown great success, especially in the classification of breast abnormalities into malignant or benign lesions. In this study, we explore the use of a deep convolutional neural network with a multi-scale module for the automated assessment of BI-RADS category on breast ultrasound images. We propose a multi-input region of interest extraction method to extract the breast region from ultrasound images, with this method avoiding the deformation of region of interest images and providing high classification performance. Moreover, we also propose an order-constrained loss function that fully considers the continuity between BI-RADS categories and shows higher performance than traditional loss functions. A large annotated dataset containing 8246 breast ultrasound images was collected to train and evaluate the proposed methods. Ablation experiments were performed to validate the effectiveness of the proposed methods. Experimental results indicate that our method can be used to mimic experienced radiologists in the assessment of the BI-RADS category of breast ultrasound images and that the automated interpretations could be acceptable in routine clinical breast ultrasound examination reports. [ABSTRACT FROM AUTHOR]