Led by Professor Fu Jin, the study addresses a critical challenge in radiation therapy: balancing the computational speed and accuracy of EPID-based dose verification.

Abstract: Because deep learning approaches are better at handling complicated noise patterns, the field of picture denoising has embraced them more and more. When it comes to handling different kinds ...

Abstract: Image denoising is a key component of digital image processing systems. The latest advances in deep learning have led to significant improvements in denoising techniques, particularly ...

GE HealthCare has received FDA Premarket Authorization for Pristina Recon DL, an innovative 3D mammography reconstruction application. Powered by artificial intelligence (AI), Pristina Recon DL ...

1 Department of Computer Science and Informatics, University of Nairobi, Nairobi, Kenya. 2 Department of Computer Science, Mountains of the Moon University, Fort Portal, Uganda. Magnetic Resonance ...

ABSTRACT: Magnetic Resonance Imaging (MRI) is commonly applied to clinical diagnostics owing to its high soft-tissue contrast and lack of invasiveness. However, its sensitivity to noise, attributable ...

This study presents a novel approach for achieving high-quality and large-scale microscopic ghost imaging by integrating deep learning-based denoising with computational ghost imaging techniques. By ...

A group of scientists led by researchers from the University of New South Wales (UNSW) in Australia has developed a novel deep-learning method for denoising outdoor electroluminescence (EL) images of ...

X-ray computed tomography (CT) is widely used in clinical practice for screening and diagnosing patients, as it enables the acquisition of high-resolution images of internal tissues and organs in a ...

In PhotoniX, researchers report a self-supervised deep learning method that denoises dynamic fluorescence images in vivo without requiring clean training data. The figure shows in vivo venule images ...