Deep learning in medical imaging and radiation therapy.

By Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen Lissak Giger. Medical Physics (Lancaster), 2018

668 citations recorded by the source corpus.

The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/eb2fcb003541b29f727588da1183bf1ca289cb73

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “Advances in medical science have significantly changed health care over the last few decades, allowing doctors to identify and treat diseases more effectively (Sahiner et al., 2019).”
Deep learning in medical image registration: a review (2019, background): “Though several review papers on deep learning in medical image analysis have been published [73, 93, 96, 105, 106, 121, 132, 182], there are very few review papers that are specific to deep learning in medical image registration [60].”
Deep learning in medical image registration: a survey (2019, background): “learning in medical image registration has not been extensively studied until the past three to four years. Though several review papers on deep learning in medical image analysis have been published [73,93,96,105,106,121,132,182], there are very few review papers that are speciﬁc to deep learning i”
Computer-Aided Diagnosis in the Era of Deep Learning (2020, methodology): “DCNN has been applied to medical image analysis for various CAD tasks despite the lack of sufficiently large medical data set compared to non-medical imaging data 21,36,37 .”
A Gentle Introduction to Deep Learning in Medical Image Processing (2018, background): “ works have appeared - almost on a daily basis - which makes it dicult to create a review paper that matches the current pace in the eld.The newest eort to summarize the entire eld was attempted in [26] listing more than 350 papers. Again, since its publication several more noteworthy works appeared”