@article{8743064,
  abstract     = {{The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging.}},
  articleno    = {{81}},
  author       = {{Decuyper, Milan and Maebe, Jens and Van Holen, Roel and Vandenberghe, Stefaan}},
  issn         = {{2197-7364}},
  journal      = {{EJNMMI PHYSICS}},
  keywords     = {{CONVOLUTIONAL NEURAL-NETWORKS,ATTENUATION CORRECTION,MRI,CT,ALGORITHMS,PET/CT,CLASSIFICATION,RECONSTRUCTION,SEGMENTATION,RADIOMICS,Artificial intelligence,Deep learning,Nuclear medicine,Medical,imaging,Radiology}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{46}},
  title        = {{Artificial intelligence with deep learning in nuclear medicine and radiology}},
  url          = {{http://doi.org/10.1186/s40658-021-00426-y}},
  volume       = {{8}},
  year         = {{2021}},
}

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