Deep learning workflow in radiology: a primer
- PMID: 32040647
- PMCID: PMC7010882
- DOI: 10.1186/s13244-019-0832-5
Deep learning workflow in radiology: a primer
Abstract
Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, classification, monitoring, and prediction. This article provides step-by-step practical guidance for conducting a project that involves deep learning in radiology, from defining specifications, to deployment and scaling. Specifically, the objectives of this article are to provide an overview of clinical use cases of deep learning, describe the composition of multi-disciplinary team, and summarize current approaches to patient, data, model, and hardware selection. Key ideas will be illustrated by examples from a prototypical project on imaging of colorectal liver metastasis. This article illustrates the workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and patient survival. Challenges are discussed, including ethical considerations, cohorting, data collection, anonymization, and availability of expert annotations. The practical guidance may be adapted to any project that requires automated medical image analysis.
Keywords: Cohorting; Convolutional neural network; Deep learning; Medical imaging; Review article.
Conflict of interest statement
Samuel Kadoury has an industry research grant from Elekta Ltd. and NuVasive inc. The other authors declare that they have no competing interests.
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References
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- Ben-Cohen Avi, Diamant Idit, Klang Eyal, Amitai Michal, Greenspan Hayit. Deep Learning and Data Labeling for Medical Applications. Cham: Springer International Publishing; 2016. Fully Convolutional Network for Liver Segmentation and Lesions Detection; pp. 77–85.
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