Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical im...
Advances in deep learning have led to the development of neural network algorithms which today rival...
Today, hospitals are producing a staggering amount of digital information, stored into electronic he...
Over the past 5 years there has been an increase in the use of convolutional neural networks in a br...
Given that neural networks have been widely reported in the research community of medical imaging, w...
Given that neural networks have been widely reported in the research community of medical imaging, w...
With medical imaging playing an increasingly prominent role in the diagnosis of disease, interests i...
The aim of this paper is to provide a snapshot of the application of neural network systems in medic...
Diagnostic medical imaging is a key tool in medical care. In recent years, thanks to advances in com...
Advances in clinical medical imaging have brought about the routine production of vast numbers of me...
The use of deep learning in medical imaging has increased rapidly over the past few years, finding a...
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. ...
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disrup...
Artificial intelligence is a sector characterized by the development of algorithms through which it ...
Deep learning models are more often used in the medical field as a result of the rapid development o...
Medical imaging, including computed tomography (CT), magnetic resonance imaging (MRI), mammography, ...
Advances in deep learning have led to the development of neural network algorithms which today rival...
Today, hospitals are producing a staggering amount of digital information, stored into electronic he...
Over the past 5 years there has been an increase in the use of convolutional neural networks in a br...
Given that neural networks have been widely reported in the research community of medical imaging, w...
Given that neural networks have been widely reported in the research community of medical imaging, w...
With medical imaging playing an increasingly prominent role in the diagnosis of disease, interests i...
The aim of this paper is to provide a snapshot of the application of neural network systems in medic...
Diagnostic medical imaging is a key tool in medical care. In recent years, thanks to advances in com...
Advances in clinical medical imaging have brought about the routine production of vast numbers of me...
The use of deep learning in medical imaging has increased rapidly over the past few years, finding a...
Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. ...
Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disrup...
Artificial intelligence is a sector characterized by the development of algorithms through which it ...
Deep learning models are more often used in the medical field as a result of the rapid development o...
Medical imaging, including computed tomography (CT), magnetic resonance imaging (MRI), mammography, ...
Advances in deep learning have led to the development of neural network algorithms which today rival...
Today, hospitals are producing a staggering amount of digital information, stored into electronic he...
Over the past 5 years there has been an increase in the use of convolutional neural networks in a br...