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Abstract
Recently, considerable progress has been made in interpreting perceptual information through artificial intelligence, allowing better interpretation of highly complex data by machines. Furthermore, the applications of artificial intelligence, represented by deep learning technology, to the fields of medical and biomedical research are increasing exponentially. In this article, we will explain the stages of deep learning algorithm development in the field of medical imaging, namely topic selection, data collection, data exploration and refinement, algorithm development, algorithm evaluation, and clinical application; we will also discuss the latest trends for each stage.
References
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Fig. 1.
Annual trend in the number of papers related to deep learning in the medical field. Results from PubMed in December 2018 using ‘deep learning' and ‘convolutional' search terms.
Fig. 3.
Relationship between the amount of noise in the dataset and the critical number of clean training examples needed to achieve high test accuracy. MNIST = Modified National Institute of Standards and Technology Adapted from Rolnick et al. arXiv preprint 2017;arXiv:1705.10694, with permission of author (7).
Fig. 4.
Correlation between ImageNet Top-1 one-crop accuracy and amount of operations. Adapted from Canziani et al. arXiv preprint 2016;arXiv:1605.07678, with permission of author (15).
Table 1.
Latest important papers on deep learning in the field of medical imaging
∗Prospective assessment. Adapted from Eric Topol. Available from: URL: https://twitter.com/EricTopol/status/1051174567882907648, with permission of author (3).
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