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Automated Deep Learning for Medical Imaging

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Artificial Intelligence in Medicine

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Abstract

Automated deep learning is a subset of machine learning. It aims to automate the machine learning workflow allowing those with limited or no coding expertise to create deep learning algorithms. It is available on a number of commercial platforms. A number of limitations still exist for automated deep learning that clinicians must be aware of. Datasets must still be curated and labelled and data governance obstacles must be navigated. Additionally, the challenges of interpretability, generalizability, and bias still exist. Automated deep learning for medical imaging has demonstrated promising results within the clinical literature when compared against bespoke machine learning models. It has generated considerable excitement as it offers the potential to democratize artificial intelligence in healthcare. In the following chapter, we will explore the role of automated deep learning within the rapidly progressing field of clinical artificial intelligence. We will examine its challenges and limitations, the principles and process of use, and what we consider the future directions of automated deep learning to be.

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Authors and Affiliations

  1. Moorfields Eye Hospital NHS Foundation Trust, London, UK

    Ciara O’Byrne, Laxmi Raja, Robbert Struyven, Edward Korot & Pearse A. Keane

  2. Byers Eye Institute, Stanford University, Palo Alto, CA, USA

    Edward Korot

  3. NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation, London, UK

    Pearse A. Keane

  4. Trinity College Dublin, Dublin, Ireland

    Ciara O’Byrne

  5. University College London, London, UK

    Laxmi Raja

Authors
  1. Ciara O’Byrne
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  2. Laxmi Raja
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  3. Robbert Struyven
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  4. Edward Korot
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  5. Pearse A. Keane
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Corresponding author

Correspondence to Pearse A. Keane .

Editor information

Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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O’Byrne, C., Raja, L., Struyven, R., Korot, E., Keane, P.A. (2021). Automated Deep Learning for Medical Imaging. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_269-1

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  • DOI: https://doi.org/10.1007/978-3-030-58080-3_269-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58080-3

  • Online ISBN: 978-3-030-58080-3

  • eBook Packages: Springer Reference MedicineReference Module Medicine

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