Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the
medical field. Deep learning (DL), in particular, has garnered significant attention due to the …

Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …

Deep learning (DL) based image processing methods have been successfully applied to
low-dose x-ray images based on the assumption that the feature distribution of the training …

Spectral computed tomography (CT) has attracted much attention in radiation dose
reduction, metal artifacts removal, tissue quantification and material discrimination. The x-ray …

The domain gap caused mainly by variable medical image quality renders a major obstacle
on the path between training a segmentation model in the lab and applying the trained …

With the advent of advances in self-supervised learning, paired clean-noisy data are no
longer required in deep learning-based image denoising. However, existing blind denoising …

Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which
suffers from the speckle noise inevitably. Deep learning has proven its superior capability in …

As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in
various clinical setting. However, OCT images are susceptible to inherent speckle noise that …

Low-dose computed tomography (LDCT) can significantly reduce the damage of X-ray to the
human body, but the reduction of CT dose will produce images with severe noise and …

Deep neural networks are commonly used for medical purposes such as image generation,
segmentation, or classification. Besides this, they are often criticized as black boxes as their …