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Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics pp 219–237Cite as

Deep Hashing and Its Application for Histopathology Image Analysis

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Content-based image retrieval (CBIR)  has attracted considerable attention for histopathology image analysis because it can provide more clinical evidence to support the diagnosis. Hashing is an important tool in CBIR due to the significant gain in both computation and storage. Because of the tremendous success of deep learning, deep hashing simultaneously learning powerful feature representations and binary codes has achieved promising performance on microscopic images. This chapter presents several popular deep hashing techniques and their applications on histopathology images. It starts introducing the automated histopathology image analysis and explaining the reasons why deep hashing is a significant and urgent need for data analysis in histopathology images. Then, it specifically discusses three popular deep hashing techniques and mainly introduces pairwise-based deep hashing. Finally, it presents their applications on histopathology image analysis.

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Author information

Authors and Affiliations

  1. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Xiaoshuang Shi & Lin Yang

Authors
  1. Xiaoshuang Shi
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  2. Lin Yang
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Correspondence to Lin Yang .

Editor information

Editors and Affiliations

  1. Bethesda Research Lab, PAII Inc., Bethesda, MD, USA

    Le Lu

  2. Nvidia Corporation, Bethesda, MD, USA

    Xiaosong Wang

  3. School of Computer Science, University of Adelaide, Adelaide, SA, Australia

    Gustavo Carneiro

  4. Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA

    Lin Yang

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Shi, X., Yang, L. (2019). Deep Hashing and Its Application for Histopathology Image Analysis. In: Lu, L., Wang, X., Carneiro, G., Yang, L. (eds) Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-13969-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-13969-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13968-1

  • Online ISBN: 978-3-030-13969-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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