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Introduction to deep learning: minimum essence required to launch a research

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Abstract

In the present article, we provide an overview on the basics of deep learning in terms of technical aspects and steps required to launch a deep learning research. Deep learning is a branch of artificial intelligence, which has been attracting interest in many domains. The essence of deep learning can be compared to teaching an elementary school student how to differentiate magnetic resonance images, and we first explain the concept using this analogy. Deep learning models are composed of many layers including input, hidden, and output ones. Convolutional neural networks are suitable for image processing as convolutional and pooling layers allow successfully performing extraction of image features. The process of conducting a research work with deep learning can be divided into the nine following steps: computer preparation, software installation, specifying the function, data collection, data edits, dataset creation, programming, program execution, and verification of results. Concerning widespread expectations, deep learning cannot be applied to solve tasks other than those set in specification; moreover, it requires a large amount of data to train and has difficulties with recognizing unknown concepts. Deep learning cannot be considered as a universal tool, and researchers should have thorough understanding of the features of this technique.

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Acknowledgements

The authors would like to thank Enago (www.enago.jp) for English language review. The authors would like to acknowledge NVIDIA for providing Figs. 11 and 12. The authors are grateful to Soichiro Tateishi, a radiological technologist in Osaka International Cancer Institute, for providing sample MR images in Figs. 1 and 2.

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Authors and Affiliations

  1. Department of Diagnostic and Interventional Radiology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, 541-8567, Japan

    Tomohiro Wataya & Katsuyuki Nakanishi

  2. Department of Artificial Intelligence Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Yuki Suzuki & Shoji Kido

  3. Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Noriyuki Tomiyama

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  1. Tomohiro Wataya
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Correspondence to Tomohiro Wataya.

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Yuki Suzuki and Shoji Kido receive research funding from Fujifilm Co., Ltd., but all the authors declare no conflicts of interest associated with this manuscript.

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Wataya, T., Nakanishi, K., Suzuki, Y. et al. Introduction to deep learning: minimum essence required to launch a research. Jpn J Radiol 38, 907–921 (2020). https://doi.org/10.1007/s11604-020-00998-2

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