Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients. This study aims to enhance the accuracy of early diagnosis of PD through non-invasive methods, with...
Low-dose computed tomography (LDCT) has gained significant attention in hospitals and clinics as a popular imaging modality for reducing the risk of x-ray radiation. However, reconstructed LDCT images often suffer from undesired noise and artifacts, ...
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations betwe...
Neural networks : the official journal of the International Neural Network Society
Jan 18, 2025
Both the single-image exposure correction (SEC) methods and multi-image exposure fusion (MEF) methods aim to obtain a well-exposed image, but from different number of input image(s). Despite their promising performance on the specific SEC or MEF task...
Neural networks : the official journal of the International Neural Network Society
Jan 18, 2025
The discrete cosine transform (DCT) has been widely used in computer vision tasks due to its ability of high compression ratio and high-quality visual presentation. However, conventional DCT is usually affected by the size of transform region and res...
International journal of pharmaceutics
Jan 18, 2025
Film-coating is a critical step in pharmaceutical manufacturing. Traditional visual inspections for film-coated tablet defect assessment are subjective, inefficient, and labor-intensive. We propose a novel approach utilizing machine learning and imag...
PURPOSE: The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag g...
Neural networks : the official journal of the International Neural Network Society
Jan 17, 2025
Low-light image enhancement (LLIE) aims to improve the visibility and illumination of low-light images. However, real-world low-light images are usually accompanied with flares caused by light sources, which make it difficult to discern the content o...
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Mic...
European journal of nuclear medicine and molecular imaging
Jan 17, 2025
PURPOSE: Radiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study inv...
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