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ABUSDet: A Novel 2.5D deep learning model for automated breast ultrasound tumor detection

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Abstract

Automated Breast Ultrasound is a highly advanced breast tumor detection modality that produces hundreds of 2D slices in each scan. However, this large number of slices poses a significant burden for physicians to review. This paper proposes a novel 2.5D tumor detection model, named “ABUSDet,” to assist physicians in automatically reviewing ABUS images and predicting the locations of breast tumors in images. At the core of this approach, a sequence of data blocks partitioned from a pre-processed 3D volume are fed to a 2.5D tumor detection model, which outputs a sequence of 2D tumor candidates. An aggregation module then clusters the 2D tumor candidates to produce the ultimate 3D coordinates of the tumors. To further improve the accuracy of the model, a novel mechanism for training deep learning models, called “Deliberate Training,” is proposed. The proposed model is trained and tested on a dataset of 87 patients with 235 ABUS volumes. It achieves sensitivities of 77.94%, 75.49%, and 65.19% at FPs/volume of 3, 2, and 1, respectively. Compared with the 2D and 3D object detection models, the proposed ABUSDet model achieves the highest sensitivity with relatively low false-positive rates.

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Due to ethical concerns, supporting data cannot be made openly available.

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Acknowledgments

We want to thank the Traditional Chinese Medicine Hospital of Guangdong Province for providing data for this study.

Funding

This work is partially supported by the National Natural Science Foundation of China (61976037), the Yongjiang Technology Innovation Project (2022A-097-G), and the Ningbo 2025 Key R&D Project (2023Z223).

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Authors and Affiliations

  1. School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia

    Xudong Song, Xiaoyang Lu, Le Cai & Wenjing Jia

  2. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia

    Xudong Song, Xiaoyang Lu, Le Cai & Wenjing Jia

  3. Thrust of Carbon Neutrality & Climate Change (CNCC), The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Gengfa Fang

  4. Computer Vision and Intelligent Perception Laboratory, University of Nottingham Ningbo China, Ningbo, China

    Xiangjian He

  5. School of Computer Science, University of Nottingham Ningbo China, Ningbo, China

    Xiangjian He

  6. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Xiaochen Fan

  7. College of Information Engineering, DalianUniversity, Dalian, China

    Zumin Wang

Authors
  1. Xudong Song
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  2. Xiaoyang Lu
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  6. Le Cai
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  7. Wenjing Jia
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  8. Zumin Wang
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Contributions

Xudong Song: Investigation, data curation, writing the manuscript, and conducting the experiments. Xiaoyang Lu: Replicated Ref [8]. Gengfa Fang: Investigation, data curation, project administration, supervision, writing, review, commentary, and revision. Xiangjian He: Funding acquisition, supervision, writing, review, commentary, and revision. Xiaochen Fan: Data analysis, review, commentary. Le cai: Data analysis, review, commentary. Wenjing Jia: Writing, review, commentary, and revision. Zumin Wang: Writing, review, commentary, and revision.

Corresponding authors

Correspondence to Gengfa Fang, Xiangjian He, Wenjing Jia or Zumin Wang.

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Song, X., Lu, X., Fang, G. et al. ABUSDet: A Novel 2.5D deep learning model for automated breast ultrasound tumor detection. Appl Intell 53, 26255–26269 (2023). https://doi.org/10.1007/s10489-023-04785-0

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  • DOI: https://doi.org/10.1007/s10489-023-04785-0

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