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Recent Advancements in Medical Imaging: A Machine Learning Approach

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Machine Learning for Intelligent Multimedia Analytics

Part of the book series: Studies in Big Data ((SBD,volume 82))

Abstract

This chapter discusses the importance of machine learning in the field of medical imaging for reconstructing medical images from the measured raw data. Besides discussing the use of machine learning for medical image reconstruction, a general overview is also provided on all the existing techniques of medical imaging. Mathematical models are provided in order to understand better the use of machine learning for reconstruction purposes. We discuss both unsupervised techniques like dictionary learning, auto-encoders, and supervised techniques which include learning of hyperparameters and various regularization methods used in deep learning models that replace various steps in iterative algorithms used for image reconstruction such as for image enhancement.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology (TIET), Patiala, 147004, India

    Nitin Dang, Shailendra Tiwari & Manju Khurana

  2. Multimedia and Information Security Research Group, ABV-Indian Institute of Information Technology and Management, Gwalior, 474015, India

    K. V. Arya

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  1. Nitin Dang
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  2. Shailendra Tiwari
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  3. Manju Khurana
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  4. K. V. Arya
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Correspondence to Shailendra Tiwari .

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Editors and Affiliations

  1. Department of Computer Science and Engineering and Information Technology, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Pardeep Kumar

  2. Department of Computer Science and Engineering, National Institute of Technology, Patna, Bihar, India

    Amit Kumar Singh

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Dang, N., Tiwari, S., Khurana, M., Arya, K.V. (2021). Recent Advancements in Medical Imaging: A Machine Learning Approach. In: Kumar, P., Singh, A.K. (eds) Machine Learning for Intelligent Multimedia Analytics. Studies in Big Data, vol 82. Springer, Singapore. https://doi.org/10.1007/978-981-15-9492-2_10

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