Automated whole-breast ultrasound tumor diagnosis using attention-inception network.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Jun 1, 2025

Abstract

PURPOSE: Automated Whole-Breast Ultrasound (ABUS) has been widely used as an important tool in breast cancer diagnosis due to the ability of this technique to provide complete three-dimensional (3D) images of breasts. To eliminate the risk of misdiagnosis, computer-aided diagnosis (CADx) systems have been proposed to assist radiologists. Convolutional neural networks (CNNs), renowned for the automatic feature extraction capabilities, have developed rapidly in medical image analysis, and this study proposes a CADx system based on 3D CNN for ABUS.

Authors

  • Jun Zhang
    First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
  • Yao-Sian Huang
  • You-Wei Wang
    Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
  • Huiling Xiang
    Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou 510060, China.
  • Xi Lin
    Department of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou 510060, China. Electronic address: linxi@sysucc.org.cn.
  • Ruey-Feng Chang
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.

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