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 …
[HTML][HTML] A review of uncertainty estimation and its application in medical imaging
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …
importance. Deep learning has shown great promise in medical imaging, but the reliability …
Current and emerging trends in medical image segmentation with deep learning
PH Conze, G Andrade-Miranda… - … on Radiation and …, 2023 - ieeexplore.ieee.org
In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …
learning has experienced a widespread interest in medical image analysis. Remarkably …
Probvlm: Probabilistic adapter for frozen vison-language models
Large-scale vision-language models (VLMs) like CLIP successfully find correspondences
between images and text. Through the standard deterministic mapping process, an image or …
between images and text. Through the standard deterministic mapping process, an image or …
PET image denoising based on denoising diffusion probabilistic model
Purpose Due to various physical degradation factors and limited counts received, PET
image quality needs further improvements. The denoising diffusion probabilistic model …
image quality needs further improvements. The denoising diffusion probabilistic model …
Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement
Image processing plays a crucial role in maximising diagnostic quality of positron emission
tomography (PET) images. Recently, deep learning methods developed across many fields …
tomography (PET) images. Recently, deep learning methods developed across many fields …
Bayescap: Bayesian identity cap for calibrated uncertainty in frozen neural networks
High-quality calibrated uncertainty estimates are crucial for numerous real-world
applications, especially for deep learning-based deployed ML systems. While Bayesian …
applications, especially for deep learning-based deployed ML systems. While Bayesian …
Hierarchical organ-aware total-body standard-dose PET reconstruction from low-dose PET and CT images
Positron emission tomography (PET) is an important functional imaging technology in early
disease diagnosis. Generally, the gamma ray emitted by standard-dose tracer inevitably …
disease diagnosis. Generally, the gamma ray emitted by standard-dose tracer inevitably …
Improving portable low-field MRI image quality through image-to-image translation using paired low-and high-field images
Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-
effective, sustainable with lower carbon emissions than superconducting high-field MRI …
effective, sustainable with lower carbon emissions than superconducting high-field MRI …
Deep learning-based PET image denoising and reconstruction: a review
This review focuses on positron emission tomography (PET) imaging algorithms and traces
the evolution of PET image reconstruction methods. First, we provide an overview of …
the evolution of PET image reconstruction methods. First, we provide an overview of …