Deep learning in medical imaging and radiation therapy.
Berkman Sahiner,Aria Pezeshk,Lubomir M. Hadjiiski,Xiaosong Wang,Karen Drukker,Kenny H. Cha,Ronald M. Summers,Maryellen L. Giger +7 more
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TLDR
The general principles of DL and convolutional neural networks are introduced, five major areas of application of DL in medical imaging and radiation therapy are surveyed, common themes are identified, methods for dataset expansion are discussed, and lessons learned, remaining challenges, and future directions are summarized.Abstract:
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.read more
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Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.
TL;DR: It is shown that in several CT segmentation tasks performance is improved significantly, especially in out-of-distribution (noncontrast CT) data, which will be valuable to medical imaging researchers to reduce manual segmentation effort and cost in CT imaging.
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Deep learning in medical image registration: a survey
TL;DR: This survey outlines the evolution of deep learning-based medical image registration in the context of both research challenges and relevant innovations in the past few years and highlights future research directions to show how this field may be possibly moved forward to the next level.
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Deep learning in medical image registration: a review.
TL;DR: A comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets is provided and the statistics of all the cited works from various aspects are analyzed, revealing the popularity and future trend ofDL-based medical image registration.
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Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey.
Sweta Bhattacharya,Praveen Kumar Reddy Maddikunta,Quoc-Viet Pham,Thippa Reddy Gadekallu,Siva Rama Krishnan S,Chiranji Lal Chowdhary,Mamoun Alazab,Jalil Piran +7 more
TL;DR: An overview of deep learning and its applications to healthcare found in the last decade is provided and three use cases in China, Korea, and Canada are presented to show deep learning applications for COVID-19 medical image processing.
Journal ArticleDOI
The 'Digital Twin' to enable the vision of precision cardiology.
Jorge Corral-Acero,Francesca Margara,Maciej Marciniak,Cristobal Rodero,Filip Loncaric,Yingjing Feng,Andrew Gilbert,Joao Filipe Fernandes,Hassaan A. Bukhari,Ali Wajdan,Manuel Villegas Martinez,Mariana Sousa Santos,Mehrdad Shamohammdi,Hongxing Luo,Philip Westphal,Paul Leeson,Paolo DiAchille,Viatcheslav Gurev,Manuel Mayr,Liesbet Geris,Pras Pathmanathan,Tina M. Morrison,Richard Cornelussen,Frits W. Prinzen,Tammo Delhaas,Ada Doltra,Marta Sitges,Edward J. Vigmond,Ernesto Zacur,Vicente Grau,Blanca Rodriguez,Espen W. Remme,Steven A. Niederer,Peter Mortier,Kristin McLeod,Mark Potse,Esther Pueyo,Alfonso Bueno-Orovio,Pablo Lamata +38 more
TL;DR: It is argued that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient.
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