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
TL;DR: 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.
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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.
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Deep learning in medical image registration: a survey
Grant Haskins,Uwe Kruger,Pingkun Yan +2 more
- 01 Feb 2020
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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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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A gentle introduction to deep learning in medical image processing
TL;DR: A gentle introduction to deep learning in medical image processing is given, proceeding from theoretical foundations to applications, including general reasons for the popularity of deep learning, including several major breakthroughs in computer science.
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TL;DR: In this article, an end-to-end deep learning approach was used to recognize either English or Mandarin Chinese speech-two vastly different languages-using HPC techniques, enabling experiments that previously took weeks to now run in days.
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