Deep learning for time series forecasting: a survey

JF Torres, D Hadjout, A Sebaa, F Martínez-Álvarez… - Big data, 2021 - liebertpub.com
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

An overview of deep learning in medical imaging focusing on MRI

AS Lundervold, A Lundervold - ar** of MR tissue parameters such as the spin‐lattice relaxation time (T1),
the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ) …

Magnetic resonance fingerprinting review part 2: Technique and directions

DF McGivney, R Boyacıoğlu, Y Jiang… - Journal of Magnetic …, 2020 - Wiley Online Library
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR‐
sensitive tissue properties with a single acquisition. There have been numerous advances in …

Deep model-based magnetic resonance parameter map** network (DOPAMINE) for fast T1 map** using variable flip angle method

Y Jun, H Shin, T Eo, T Kim, D Hwang - Medical Image Analysis, 2021 - Elsevier
Quantitative tissue characteristics, which provide valuable diagnostic information, can be
represented by magnetic resonance (MR) parameter maps using magnetic resonance …

A novel forecasting strategy for improving the performance of deep learning models

IE Livieris - Expert Systems with Applications, 2023 - Elsevier
In this research, a new strategy is introduced for the development of robust, efficient and
reliable deep learning time-series models, which is based on a sophisticated algorithmic …