Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
[HTML][HTML] Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
Prior-guided image reconstruction for accelerated multi-contrast MRI via generative adversarial networks
Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available
for diagnosis. Yet, excessive scan times associated with additional contrasts may be a …
for diagnosis. Yet, excessive scan times associated with additional contrasts may be a …
[HTML][HTML] Applications of deep learning to neuro-imaging techniques
Many clinical applications based on deep learning and pertaining to radiology have been
proposed and studied in radiology for classification, risk assessment, segmentation tasks …
proposed and studied in radiology for classification, risk assessment, segmentation tasks …
[HTML][HTML] Variability and standardization of quantitative imaging: monoparametric to multiparametric quantification, radiomics, and artificial intelligence
Radiological images have been assessed qualitatively in most clinical settings by the expert
eyes of radiologists and other clinicians. On the other hand, quantification of radiological …
eyes of radiologists and other clinicians. On the other hand, quantification of radiological …
mustGAN: multi-stream generative adversarial networks for MR image synthesis
Multi-contrast MRI protocols increase the level of morphological information available for
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …
Generative adversarial networks to synthesize missing T1 and FLAIR MRI sequences for use in a multisequence brain tumor segmentation model
Background Missing MRI sequences represent an obstacle in the development and use of
deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing …
deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing …
[HTML][HTML] The role of generative adversarial networks in brain MRI: a scoping review
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …
made available. Generative adversarial networks (GANs) showed a lot of potential to …
Artificial intelligence applications in psychoradiology
One important challenge in psychiatric research is to translate findings from brain imaging
research studies that identified brain alterations in patient groups into an accurate diagnosis …
research studies that identified brain alterations in patient groups into an accurate diagnosis …
[HTML][HTML] Convolutional-neural network-based image crowd counting: Review, categorization, analysis, and performance evaluation
Traditional handcrafted crowd-counting techniques in an image are currently transformed
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …
via machine-learning and artificial-intelligence techniques into intelligent crowd-counting …