Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Medical image analysis is performed by analyzing images obtained by medical imaging
systems to solve clinical problems. The purpose is to extract effective information and …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel
coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid …

As the eye's main load-bearing connective tissue, the sclera is centrally important to vision.
In addition to cooperatively maintaining refractive status with the cornea, the sclera must …

Medical image segmentation is a typical task in medical image processing and critical
foundation in medical image analysis. U-Net is well-liked in medical image segmentation …

Phase unwrapping is an indispensable step for many optical imaging and metrology
techniques. The rapid development of deep learning has brought ideas to phase …

Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that
provides three-dimensional, information-rich vascular images. With numerous studies …

We look at the recent application of deep learning (DL) methods in automated fine-grained
segmentation of spectral domain optical coherence tomography (OCT) images of the retina …

Deep learning methods have enabled a fast, accurate and automated approach for retinal
layer segmentation in posterior segment OCT images. Due to the success of semantic …