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A review of deep learning in medical imaging Imaging traits technology trends case studies with progress highlights and future promises

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Document pages: 20 pages

Abstract: Since its renaissance, deep learning has been widely used in various medicalimaging tasks and has achieved remarkable success in many medical imagingapplications, thereby propelling us into the so-called artificial intelligence(AI) era. It is known that the success of AI is mostly attributed to theavailability of big data with annotations for a single task and the advances inhigh performance computing. However, medical imaging presents unique challengesthat confront deep learning approaches. In this survey paper, we first presenttraits of medical imaging, highlight both clinical needs and technicalchallenges in medical imaging, and describe how emerging trends in deeplearning are addressing these issues. We cover the topics of networkarchitecture, sparse and noisy labels, federating learning, interpretability,uncertainty quantification, etc. Then, we present several case studies that arecommonly found in clinical practice, including digital pathology and chest,brain, cardiovascular, and abdominal imaging. Rather than presenting anexhaustive literature survey, we instead describe some prominent researchhighlights related to these case study applications. We conclude with adiscussion and presentation of promising future directions.

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