Development of metaverse for intelligent healthcare
The metaverse integrates physical and virtual realities, enabling humans and their avatars to
interact in an environment supported by technologies such as high-speed internet, virtual …
interact in an environment supported by technologies such as high-speed internet, virtual …
Low‐dose CT for the detection and classification of metastatic liver lesions: results of the 2016 low dose CT grand challenge
CH McCollough, AC Bartley, RE Carter… - Medical …, 2017 - Wiley Online Library
Purpose Using common datasets, to estimate and compare the diagnostic performance of
image‐based denoising techniques or iterative reconstruction algorithms for the task of …
image‐based denoising techniques or iterative reconstruction algorithms for the task of …
CoreDiff: Contextual error-modulated generalized diffusion model for low-dose CT denoising and generalization
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon
starvation and electronic noise. Recently, some works have attempted to use diffusion …
starvation and electronic noise. Recently, some works have attempted to use diffusion …
From papyrus leaves to bioprinting and virtual reality: history and innovation in anatomy
B Bisht, A Hope, MK Paul - Anatomy & Cell Biology, 2019 - synapse.koreamed.org
The human quest to master the anatomy and physiology of living systems started as early as
1600 BC, with documents from the Greeks, Indians, and Romans presenting the earliest …
1600 BC, with documents from the Greeks, Indians, and Romans presenting the earliest …
CoCoDiff: a contextual conditional diffusion model for low-dose CT image denoising
Convolutional neural networks (CNNs) have been widely used for low-dose CT (LDCT)
image denoising. To alleviate the over-smoothing effect caused by conventional mean …
image denoising. To alleviate the over-smoothing effect caused by conventional mean …
[HTML][HTML] Report of the Medical Image De-Identification (MIDI) Task Group-Best Practices and Recommendations
1.4 Support This project has been funded in whole or in part with Federal funds from the
National Cancer Institute, National Institutes of Health, under Contract No …
National Cancer Institute, National Institutes of Health, under Contract No …
Blind CT image quality assessment via deep learning framework
Computed tomography (CT) images will be severely damaged from low-mAs acquisition
conditions. Seriously degraded CT images may lead to diagnostic bias in clinics. It is vital to …
conditions. Seriously degraded CT images may lead to diagnostic bias in clinics. It is vital to …
[HTML][HTML] Artificial intelligence generated content (AIGC) in medicine: A narrative review
L Shao, B Chen, Z Zhang, Z Zhang… - Mathematical …, 2024 - aimspress.com
Recently, artificial intelligence generated content (AIGC) has been receiving increased
attention and is growing exponentially. AIGC is generated based on the intentional …
attention and is growing exponentially. AIGC is generated based on the intentional …
CMC-diffusion: Curve matching correction diffusion model for LDCT denoising
J Xia, M Yan, X Yang, X Zhang, Z Tao - Biomedical Signal Processing and …, 2025 - Elsevier
Computed Tomography (CT) scans due to their short examination time and high accuracy,
have become a widely used medical diagnostic tool in clinical medicine. In order to reduce …
have become a widely used medical diagnostic tool in clinical medicine. In order to reduce …
Combined global and local information for blind CT image quality assessment via deep learning
Image quality assessment (IQA) is an important step to determine whether the computed
tomography (CT) images are suitable for diagnosis. Since the high dose CT images are …
tomography (CT) images are suitable for diagnosis. Since the high dose CT images are …