Real-world single image super-resolution: A brief review
Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …
image from a low-resolution (LR) observation, has been an active research topic in the area …
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 …
Convergence of artificial intelligence and neuroscience towards the diagnosis of neurological disorders—a sco** review
Artificial intelligence (AI) is a field of computer science that deals with the simulation of
human intelligence using machines so that such machines gain problem-solving and …
human intelligence using machines so that such machines gain problem-solving and …
Medical image super-resolution reconstruction algorithms based on deep learning: A survey
D Qiu, Y Cheng, X Wang - Computer Methods and Programs in …, 2023 - Elsevier
Background and objective With the high-resolution (HR) requirements of medical images in
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
clinical practice, super-resolution (SR) reconstruction algorithms based on low-resolution …
Artificial intelligence in medical imaging
JC Gore - Magnetic resonance imaging, 2020 - Elsevier
The medical specialty radiology has experienced a number of extremely important and
influential technical developments in the past that have affected how medical imaging is …
influential technical developments in the past that have affected how medical imaging is …
SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning
High resolution magnetic resonance (MR) images are desired in many clinical and research
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …
Autoencoder based self-supervised test-time adaptation for medical image analysis
Deep neural networks have been successfully applied to medical image analysis tasks like
segmentation and synthesis. However, even if a network is trained on a large dataset from …
segmentation and synthesis. However, even if a network is trained on a large dataset from …
Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of
Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast …
Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast …
Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model
J Li, S Li, X Li, S Miao, C Dong, C Gao, X Liu, D Hao… - European …, 2023 - Springer
Objectives Automatic bone lesions detection and classifications present a critical challenge
and are essential to support radiologists in making an accurate diagnosis of bone lesions. In …
and are essential to support radiologists in making an accurate diagnosis of bone lesions. In …
[HTML][HTML] Emerging trends in fast MRI using deep-learning reconstruction on undersampled k-space data: a systematic review
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …