## 5 Conclusions  
ABUS provides high-resolution imaging of internal anatomy in a standardized, reproducible, and reliable manner, significantly enhancing BC screening and diagnosis. However, when applying deep learning-based volumetric image analysis to ABUS, a critical challenge remains on how to effectively balance computational effectiveness and efficiency. This study introduces a novel soft voting strategy that integrates both tumor sizes and voxel-level malignancy probabilities, offering a more nuanced approach to classification. This strategy can serve as a useful post-processing step for 2D-input deep networks, with the potential to improve the accuracy and reliability of tumor assessments, thereby supporting diagnostic performance.  
Conceptualization, S.Y., X.L. and Q.S.; Data curation, S.Y., X.L. and S.Z.; Formal analysis, S.Y., Y.X. and Q.S.; Funding acquisition, Y.X. and Q.S.; Investigation, S.Y., Y.X. and Q.S.; Methodology, S.Y., X.L. and S.Z.; Project administration, Y.X. and Q.S.; Software, S.Y., X.L. and S.Z.; Supervision, Q.S.; Validation, S.Z. and Y.X.; Visualization, X.L and S.Z.; Writing--original draft, S.Y. and X.L.; Writing--review and editing, Y.X. and Q.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work was in part supported by National Key Research and Develop Program of China (Grant No. 2022 ZD0115901, and 2022 YFC2409000), National Natural Science Foundation of China (Grant No. 62177007, U20A20373, and 82202954), China-Central Eastern European Countries High Education Joint Education Project (Grant No. 202012), Medium- and Long-term Technology Plan for Radio, Television and Online Audiovisual (Grant No. ZG23011), and Architecture Design and Implementation of Building Common Business Services based on Rapid Development Platform (Grant No. 2222000247). The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset supporting the current study is available online (the Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound (ABUS) 2023, namely TDSC-ABUS 2023, [[https://tdsc-abus2023.grand-challenge.org](https://tdsc-abus2023.grand-challenge.org)]([https://tdsc-abus2023.grand-challenge.org](https://tdsc-abus2023.grand-challenge.org)), accessed on 5 December 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BC & breast cancer \\ MAM & mammography \\ US & ultrasound \\ HHUS & hand-held ultrasound \\
3D & three-dimensional \\ ABUS & automated breast ultrasound \\ TDSC-ABUS2023 & Tumor Detection, Segmentation and Classification Challenge on ABUS 2023 \\ SV & soft voting \\ CNN & convolutional neural network \\ MHCA & multi-head convolutional attention \\ LFFN & local feed-forward network \\ ESA & efficient self-attention \\ ECB & efficient convolutional block \\ LTB & local-token block \\ HFF & hierarchical feature fusion \\ MLP & multi-layer perceptron \\ SSM & structured state-space model \\ TP & true positive \\ TN & true negative \\ FP & false positive \\ FN & false negative \\ ACC & accuracy \\ SEN & sensitivity \\ SPE & specificity \\ AUC & area under the curve \\ ROC & receiver operating characteristic \\ \(t\)-SNE & \(t\)-distributed stochastic neighbor embedding \\ \end{tabular}