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Applying Attention Mechanism and Deep Neural Network for Medical Object Segmentation and Classification in X-Ray Fluoroscopy Images

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Human Brain and Artificial Intelligence (HBAI 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1072))

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Abstract

We study how to apply attention mechanism and deep neural network for real-time segmentation and classification of balloon objects from X-ray fluoroscopy images during percutaneous balloon compression (PBC) surgical procedures. Fast and accurate identification of balloon shape and its relative location to the Meckel’s cave can be of significant benefit to the success of the PBC procedure. In this work, we combine the most successful region-based convolutional neural network pipeline with attention mechanism to address these challenges.

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Author information

Authors and Affiliations

  1. Weber State University, Ogden, UT, 84408, USA

    Yong Zhang & Christopher Yencha

  2. Yidu Cloud Technology Co. Ltd., Beijing, China

    Jun Yan

  3. The People’s Hospital of Liaoning Province, Shenyang, China

    Haitao Huang

Authors
  1. Yong Zhang
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  2. Jun Yan
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  3. Haitao Huang
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  4. Christopher Yencha
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Corresponding author

Correspondence to Haitao Huang .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    An Zeng

  2. Guangdong Construction Polytechnic, Guangzhou, China

    Dan Pan

  3. South China Normal University, Guangzhou, China

    Tianyong Hao

  4. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Daoqiang Zhang

  5. University of Notre Dame, South Bend, IN, USA

    Yiyu Shi

  6. Simon Fraser University, Vancouver, BC, Canada

    Xiaowei Song

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Zhang, Y., Yan, J., Huang, H., Yencha, C. (2019). Applying Attention Mechanism and Deep Neural Network for Medical Object Segmentation and Classification in X-Ray Fluoroscopy Images. In: Zeng, A., Pan, D., Hao, T., Zhang, D., Shi, Y., Song, X. (eds) Human Brain and Artificial Intelligence. HBAI 2019. Communications in Computer and Information Science, vol 1072. Springer, Singapore. https://doi.org/10.1007/978-981-15-1398-5_7

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  • DOI: https://doi.org/10.1007/978-981-15-1398-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1397-8

  • Online ISBN: 978-981-15-1398-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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