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

Since its renaissance, deep learning 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 t...

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Vydané v:Proceedings of the IEEE Ročník 109; číslo 5; s. 820 - 838
Hlavní autori: Zhou, S. Kevin, Greenspan, Hayit, Davatzikos, Christos, Duncan, James S., Van Ginneken, Bram, Madabhushi, Anant, Prince, Jerry L., Rueckert, Daniel, Summers, Ronald M.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Annotations
Artificial intelligence
Biomedical imaging
Case studies
Clinical diagnosis
Computed tomography
Computer architecture
Deep learning
Deep learning (DL)
Digital imaging
Diseases
Image segmentation
Literature reviews
Medical diagnostic imaging
Medical imaging
Medical services
Network architecture
survey
Trends
deep learning
survey
Medical imaging
ISSN:0018-9219, 1558-2256
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Popis
Shrnutí:Since its renaissance, deep learning 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 deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2021.3054390