## 1 Introduction  
Breast cancer (BC) is the most diagnosed cancer among women. It caused about 2.26 million new BC cases globally in 2020 as the most prevalent cancer [1]. The incidence of female BC continued a slow increase during 2014 through 2018 by 0.5% annually [2]. In developed countries, such as in the United States, BC incidence rates have risen in most of the past four decades, largely driven by localized-stage and hormone receptor-positive disease [3]. Compared to developed countries, the rates are relatively lower in China, while the actual number of new cases and deaths has been considerable [4]. BC poses a serious threat to women's health and places a heavy burden on the finance and healthcare systems. Improvements in medical imaging, cancer screening and diagnosis, therapeutic planning and delivery, and follow-up monitoring contribute to the wide coverage of the health system, insurance, and advances in treatment and management [1, 2, 3, 4]. Medical imaging devices are routinely used for BC screening and diagnosis, including mammography (MAM) and ultrasound (US). MAM remains the gold standard due to its high-resolution imaging of internal anatomy and its sensitivity for early-stage BCdetection [5]. Its effectiveness has been demonstrated in large-scale clinical trials, leading to improved treatment outcomes, higher survival rates, and reduced mortality through early intervention [2; 3]. However, it is associated with issues such as over-diagnosis, radiation exposure, and decreased sensitivity in patients with dense breast tissue. MAM has been found to be less suitable for young women and Asian women, who often have denser breast tissue [6]. Hand-held US (HHUS) eliminates the risk of radiation exposure and provides more detailed imaging for women with dense breast tissue. It effectively differentiates between solid tumors and fluid-filled cysts, thereby reducing unnecessary biopsies [7]. To address operator dependence, limited field of view, and to enhance imaging quality, three-dimensional (3D) ultrasound imaging has been developed [8; 9; 10; 11]. As an emerging technique, 3D automated breast ultrasound (ABUS) serves as a supplementary method for evaluating women with heterogeneously and extremely dense breasts [11]. It offers several advantages in screening and diagnostic settings, including an increased BC detection rate, improved workflow efficiency, and reduced examination time. Notably, ABUS separates image acquisition from image interpretation, thereby decreasing operator dependence and time cost. [PERSON] and [PERSON] investigated the performance of ABUS and HHUS in a large cohort of 1886 women and found that ABUS enhances the sensitivity of cancer detection [12]. Additionally, [PERSON] et al. conducted a retrospective clinical study comparing the performance of ABUS and HHUS in cancer diagnosis, identifying that ABUS results in lower recall and biopsy rates, as it provides multiple perspectives of suspicious regions for examination [13]. Therefore, ABUS has significant potential to be routinely used as a standardized, reproducible, and reliable tool for whole-breast visualization, screening, and diagnosis [12; 13; 14], offering added value for patients with dense breasts [10; 11]. Accurate diagnosis of breast lesions observed in ABUS enables the determination of tumor malignancy and the formulation of treatment plans. However, few deep learning models have been developed for this purpose [15; 16]. [PERSON] et al. extracted spiculation patterns in coronal planes and designed spiculation and other characteristic features for classifying lesions as malignant or benign using support vector machine [17]. [PERSON] et al. modified the Inception-v3 architecture for efficient feature extraction, integrating features from both transverse and coronal views for cancer diagnosis [18]. [PERSON] et al. combined residual blocks, capsule neural structures, and group normalization for ABUS tumor classification [19]. [PERSON] et al.