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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …
and has achieved remarkable success in many medical imaging applications, thereby …
Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting
new knowledge. By using DL, the extraction of advanced data representations and …
new knowledge. By using DL, the extraction of advanced data representations and …
An overview of deep learning in medical imaging focusing on MRI
What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
medical image analysis? Machine learning has witnessed a tremendous amount of attention …
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …
including image acquisition, analysis and interpretation, and for the extraction of clinically …
[HTML][HTML] A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease
Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder. Mild
cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can …
cognitive impairment (MCI) is a clinical precursor of AD. Although some treatments can …
Deep learning in Alzheimer's disease: diagnostic classification and prognostic prediction using neuroimaging data
Deep learning, a state-of-the-art machine learning approach, has shown outstanding
performance over traditional machine learning in identifying intricate structures in complex …
performance over traditional machine learning in identifying intricate structures in complex …
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
[HTML][HTML] Accurate brain age prediction with lightweight deep neural networks
Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …
but the prediction performance is often limited by training-dataset size and computing …
Convolutional neural networks for radiologic images: a radiologist's guide
Deep learning has rapidly advanced in various fields within the past few years and has
recently gained particular attention in the radiology community. This article provides an …
recently gained particular attention in the radiology community. This article provides an …
Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …
diagnosis of neurodegenerative disorders, eg, Alzheimer's disease (AD), due to its …