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Medical Image Enhancement Using Deep Learning

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Deep Learning in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 171))

Abstract

This chapter aims to introduce medical image enhancement technology using 2-dimentional and 3-dimentional deep learning. The article starts from basic methods about convolutional layer, deconvolution layer, loss function and evaluation functions for beginners to easily understand. Then, typical state-of-the-art super-resolution methods using 2D or 3D convolution neural networks will be introduced. From the experimental results of the network introduced in this chapter, readers can not only make a comparison about the network structure but also have a general understanding about network performance.

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Acknowledgements

This work is supported in part by the Grant in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant Nos. 18K18078, 18H03267, in part by Zhejiang Lab Program under the Grant No. 2018DG0ZX01.

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Authors and Affiliations

  1. Ritsumeikan University, Kusatsu, Japan

    Yinhao Li, Yutaro Iwamoto & Yen-Wei Chen

Authors
  1. Yinhao Li
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  2. Yutaro Iwamoto
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  3. Yen-Wei Chen
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Corresponding author

Correspondence to Yen-Wei Chen .

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Editors and Affiliations

  1. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan

    Yen-Wei Chen

  2. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, NSW, Australia

    Lakhmi C. Jain

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Li, Y., Iwamoto, Y., Chen, YW. (2020). Medical Image Enhancement Using Deep Learning. In: Chen, YW., Jain, L. (eds) Deep Learning in Healthcare. Intelligent Systems Reference Library, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-030-32606-7_4

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