A gentle introduction to deep learning in medical image processing [Eine sanfte Einführung in Tiefes Lernen in der Medizinischen Bildverarbeitung]

Maier A, Syben-Leisner C, Lasser T, Riess C (2019)


Publication Type: Journal article, Review article

Publication year: 2019

Journal

Book Volume: 29

Pages Range: 86-101

Journal Issue: 2

DOI: 10.1016/j.zemedi.2018.12.003

Abstract

This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

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How to cite

APA:

Maier, A., Syben-Leisner, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing [Eine sanfte Einführung in Tiefes Lernen in der Medizinischen Bildverarbeitung]. Zeitschrift für Medizinische Physik, 29(2), 86-101. https://dx.doi.org/10.1016/j.zemedi.2018.12.003

MLA:

Maier, Andreas, et al. "A gentle introduction to deep learning in medical image processing [Eine sanfte Einführung in Tiefes Lernen in der Medizinischen Bildverarbeitung]." Zeitschrift für Medizinische Physik 29.2 (2019): 86-101.

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