## 1 Introduction
designed a multi-task learning framework for joint ABUS tumor segmentation and classification, incorporating multi-scale feature extraction and iterative feature refinement [20]. [PERSON] et al. added an automatic segmentation network for morphological analysis along with ResNet-based tumor diagnosis [21]. [PERSON] et al. proposed a multi-view attention network that utilizes a localization unit for lesion region cropping and a classification unit for malignancy prediction based on the Transformer architecture [22]. [PERSON] et al. developed a 2.5D deep model that fine-tunes a pre-trained network for tumor classification, using the ten slices with the largest lesion regions along with adjacent slices as input [23]. Therefore, there is an urgent need to explore deep learning networks for ABUS tumor classification. In 3D high-resolution medical image analysis using deep learning, a key challenge in ABUS tumor classification is achieving a balance between time efficiency and classification accuracy. However, 3D-input deep networks face several significant problems [20]. At first, high dimensionality of 3D images increases data processing complexity, computational demands, and resource intensity compared to 2D images. Secondly, ABUS tumors exhibit a wide range of shapes, sizes, and characteristics, and therefore, it is difficult to develop a model that can accurately classify all types of lesions. Thirdly, the quality of ABUS images is influenced by the imaging devices used, and these images often suffer from noise and various artifacts [15]. Fourthly, many clinical settings require rapid image processing and analysis for timely decision-making, which adds pressure to achieve high classification accuracy while maintaining fast processing speeds. One of the most significant challenges is the limited availability of annotated datasets; very few ABUS cases are publicly accessible for training [16]. Specifically, an overview [15] of recent advancements in BC image analysis indicates that one 3D ABUS database with 100 volumetric cases is accessible for algorithm development and fair comparison. In practice, using volumetric images as input for deep networks necessitates iterative optimization of hyper-parameters, which requires a large-scale, high-quality database and leads to significant time costs [19; 20]. While noisy data can be utilized for model training, the robustness of these models must be thoroughly examined in the context of medical image analysis [24; 25; 26]. On the other hand, while using slice images as input allows for faster slice-wise lesion predictions, effectively combining the slice-wise probabilities for benign and malignant predictions remains an open question. In summary, deep learning-based 3D-input ABUS tumor classification faces challenges related to high time costs, a lack of sufficient training samples, and the complexity of hyper-parameter optimization. To the best of our knowledge, few studies have specifically addressed the aforementioned issues in ABUS tumor classification [16]. In this study, a soft voting (SV) strategy is proposed using voxel-level weighting of slice prediction. It performs as a post-processing step after image slice prediction via a 2D-input deep learning model. It should be noted that, in pediatric brain tumor classification, [PERSON] et al. also propose soft voting for per-slice class prediction [27]. However, there are key differences between the SV strategies in our work and that in Ref. [27]. Firstly, our SV is directly related to the prediction for per-volume classes, while in Ref. [27], it pertains to slice-level class prediction. Secondly, our SV operates on the predicted probabilities of the benign and malignant classes for each voxel, whereas that in [27] aims to predict each slice as belonging to one of three classes. Thirdly, our SV uses only a single classification model, while the SV in [27] involves multiple classification models.