AttentionNet: Learning where to focus via attention mechanism for anatomical segmentation of whole breast ultrasound images

Hang Li, Jie Zhi Cheng, Yi Hong Chou, Jing Qin, Shan Huang, Baiying Lei

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)


The main challenges of the anatomical segmentation of automated whole breast ultrasound (AWBUS) image are shadow effect, blurred boundary, low contrast and large target. To tackle them, a novel and effective framework named AttentionNet is developed via self-attention mechanism during both feature extraction and up-sampling phase. Specifically, features are firstly extracted based on ResNeXt-50 to explore the information of intra-channels. With the goal of extracting features and utilizing channel information effectively, a module named spatial attention refinement (SAR) is devised using the basic ResNeXt-50 module (a.k.a., ResNeXt-SAR). Then, a weighted up-sampling block (WUB) module for precise pixel localization is designed by introducing high-level semantic concept during up-sampling phase, playing an important role in guiding the low-level features by the category information. The extensive experiments are conducted on AWBUS image for multi-class image segmentation. Our proposed AttentionNet achieves the superior results over the state-of-the-art approaches and may help to assist the calculation of breast density.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538636411
Publication statusPublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019


  • Automated whole breast ultrasound
  • Breast segmentation
  • Deep convolutional neural network
  • Self-attention mechanism

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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