Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
SK Zhou, H Greenspan, C Davatzikos… - Proceedings of the …, 2021 - ieeexplore.ieee.org
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 …
[HTML][HTML] Assessment of non-invasive blood pressure prediction from ppg and rppg signals using deep learning
F Schrumpf, P Frenzel, C Aust, G Osterhoff, M Fuchs - Sensors, 2021 - mdpi.com
Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP)
measurement is interesting for various reasons. First, PPG can easily be measured using …
measurement is interesting for various reasons. First, PPG can easily be measured using …
Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence
Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
BolT: Fused window transformers for fMRI time series analysis
Deep-learning models have enabled performance leaps in analysis of high-dimensional
functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for …
functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for …
[HTML][HTML] Medical image analysis using deep learning algorithms
M Li, Y Jiang, Y Zhang, H Zhu - Frontiers in Public Health, 2023 - frontiersin.org
In the field of medical image analysis within deep learning (DL), the importance of
employing advanced DL techniques cannot be overstated. DL has achieved impressive …
employing advanced DL techniques cannot be overstated. DL has achieved impressive …
Big data analytics in medical engineering and healthcare: methods, advances and challenges
L Wang, CA Alexander - Journal of medical engineering & …, 2020 - Taylor & Francis
Big data analytics are gaining popularity in medical engineering and healthcare use cases.
Stakeholders are finding big data analytics reduce medical costs and personalise medical …
Stakeholders are finding big data analytics reduce medical costs and personalise medical …
Improving diagnostic accuracy in low-dose SPECT myocardial perfusion imaging with convolutional denoising networks
AJ Ramon, Y Yang, PH Pretorius… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
Lowering the administered dose in SPECT myocardial perfusion imaging (MPI) has become
an important clinical problem. In this study we investigate the potential benefit of applying a …
an important clinical problem. In this study we investigate the potential benefit of applying a …
Deep CNN models for predicting COVID-19 in CT and x-ray images
Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread
worldwide and with no automatic model to reliably detect its presence from images. We aim …
worldwide and with no automatic model to reliably detect its presence from images. We aim …
Experimental setup employed in the operating room based on virtual and mixed reality: analysis of pros and cons in open abdomen surgery
Currently, surgeons in operating rooms are forced to focus their attention both on the
patient's body and on flat low‐quality surgical monitors, in order to get all the information …
patient's body and on flat low‐quality surgical monitors, in order to get all the information …