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Smart Health in Medical Image Analysis

In: Optimization in Large Scale Problems

Author

Listed:
  • Haifeng Wang

    (State University of New York at Binghamton)

  • Qianqian Zhang

    (State University of New York at Binghamton)

  • Daehan Won

    (State University of New York at Binghamton)

  • Sang Won Yoon

    (State University of New York at Binghamton)

Abstract

Medical imaging can facilitate diagnoses, treatment, and surgical planning, and increase clinical productivity. However, manual assessments of medical images require time-consuming works and lead to subjective conclusions. Through application of artificial intelligence (AI), automatic medical image analysis can be achieved to improve the accuracy and efficiency of healthcare services. This chapter describes two deep learning-based AI techniques for medical image analysis, e.g., tissue classification and medical image data augmentation. For each example, the algorithms are described first. Then the experiment results are presented to show the potential performance of using AI to enhance smart health. Conclusions and future directions are summarized at the end of the chapter.

Suggested Citation

  • Haifeng Wang & Qianqian Zhang & Daehan Won & Sang Won Yoon, 2019. "Smart Health in Medical Image Analysis," Springer Optimization and Its Applications, in: Mahdi Fathi & Marzieh Khakifirooz & Panos M. Pardalos (ed.), Optimization in Large Scale Problems, pages 221-242, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-28565-4_20
    DOI: 10.1007/978-3-030-28565-4_20
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