Deep Learning for Breast Lesion Classification, Detection, and Segmentation in Ultrasound Imaging: A Systematic Review

Ultrasound imaging has become an important tool for breast cancer screening. With the growing application of deep learning in medical imaging, numerous methods have been developed to enhance the accuracy and efficiency of breast ultrasound interpretation. However, a systematic understanding of these approaches remains limited. This review aims to comprehensively evaluate the development of deep learning techniques for breast ultrasound-based auxiliary diagnosis. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured search was conducted across 11 academic databases to identify studies published between January 2016 and October 2024. A total of 2,779 records were initially retrieved. After duplicate removal, eligibility screening, and quality assessment, 65 studies were included in the review. These studies were categorized into three main subtopics: 24 focused on lesion classification, 14 on object detection, and 27 on lesion segmentation. The categorization reflects the primary clinical tasks in breast ultrasound diagnosis, including identifying lesion types, localizing lesion regions, and delineating lesion boundaries. Recent evidence shows that deep learning-based methods have achieved promising performance through strategies such as feature enhancement, multi-scale feature extraction, and few-shot learning. Finally, this review discusses future work and challenges in applying deep learning to auxiliary diagnosis in breast ultrasound imaging. This paper presents a systematic review that summarizes current studies on deep learning-based auxiliary diagnosis in breast ultrasound imaging.