Applications of artificial intelligence in cardiovascular imaging
M Sermesant, H Delingette, H Cochet, P Jaïs… - Nature Reviews …, 2021 - nature.com
Research into artificial intelligence (AI) has made tremendous progress over the past
decade. In particular, the AI-powered analysis of images and signals has reached human …
decade. In particular, the AI-powered analysis of images and signals has reached human …
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 …
Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study
Abstract The novel coronavirus (COVID-19) has significantly spread over the world and
comes up with new challenges to the research community. Although governments imposing …
comes up with new challenges to the research community. Although governments imposing …
[HTML][HTML] A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions
B Pandey, DK Pandey, BP Mishra… - Journal of King Saud …, 2022 - Elsevier
The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
[PDF][PDF] Forecasting of photovoltaic solar power production using LSTM approach
Solar-based energy is becoming one of the most promising sources for producing power for
residential, commercial, and industrial applications. Energy production based on solar …
residential, commercial, and industrial applications. Energy production based on solar …
[HTML][HTML] Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study
Accurate wind power prediction is critical for efficient grid management and the integration of
renewable energy sources into the power grid. This study presents an effective deep …
renewable energy sources into the power grid. This study presents an effective deep …
Fast deep learning reconstruction techniques for preclinical magnetic resonance fingerprinting
We propose a deep learning (DL) model and a hyperparameter optimization strategy to
reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) …
reconstruct T1 and T2 maps acquired with the magnetic resonance fingerprinting (MRF) …
High-quality MR fingerprinting reconstruction using structured low-rank matrix completion and subspace projection
Due to the capability of fast multiparametric quantitative imaging, magnetic resonance
fingerprinting (MRF) is becoming a promising quantitative magnetic resonance imaging …
fingerprinting (MRF) is becoming a promising quantitative magnetic resonance imaging …
Learned tensor low-CP-rank and Bloch response manifold priors for non-Cartesian MRF reconstruction
Magnetic resonance fingerprinting (MRF) can rapidly perform simultaneous imaging of
multiple tissue parameters. However, the rapid acquisition schemes used in MRF inevitably …
multiple tissue parameters. However, the rapid acquisition schemes used in MRF inevitably …
Only‐train‐once MR fingerprinting for B0 and B1 inhomogeneity correction in quantitative magnetization‐transfer contrast
Purpose To develop a fast, deep‐learning approach for quantitative magnetization‐transfer
contrast (MTC)–MR fingerprinting (MRF) that simultaneously estimates multiple tissue …
contrast (MTC)–MR fingerprinting (MRF) that simultaneously estimates multiple tissue …