Radiological images and machine learning: Trends, perspectives, and prospects

The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities...

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Vydané v:Computers in biology and medicine Ročník 108; s. 354 - 370
Hlavní autori: Zhang, Zhenwei, Sejdić, Ervin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.05.2019
Elsevier Limited
Predmet:
Accuracy
Algorithms
Alzheimer's disease
Artificial intelligence
Brain
Cancer
Clinical decision making
Cognitive ability
Computed tomography
Decision making
Deep Learning
Deep neural network
Fractures
Gamma rays
Human performance
Humans
Image management
Image processing
Image Processing, Computer-Assisted
Image registration
Image retrieval
Image segmentation
Imaging modalities
Keywords
Learning algorithms
Machine learning
Magnetic Resonance Imaging
Mammography
Medical imaging
Medical research
Models, Theoretical
Neural networks
Neuroimaging
Neurological diseases
NMR
Nuclear magnetic resonance
Positron emission
Positron emission tomography
Principal components analysis
Radiology
Ultrasonic imaging
X-rays
Deep learning
Deep neural network
Imaging modalities
Machine learning
ISSN:0010-4825, 1879-0534, 1879-0534
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Popis
Shrnutí:The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
Bibliografia:ObjectType-Article-1
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2019.02.017