A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

@article{Zhou2021ARO,
  title={A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises},
  author={S. Kevin Zhou and Hayit Greenspan and Christos Davatzikos and James S. Duncan and Bram van Ginneken and Anant Madabhushi and J. Prince and Daniel Rueckert and Ronald M. Summers},
  journal={Proceedings of the IEEE},
  year={2021},
  volume={109},
  pages={820-838}
}
Since its renaissance, deep learning (DL) has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high-performance computing. However, medical imaging presents unique challenges that confront DL approaches. In… Expand

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