The development of medical imaging techniques has greatly supported clinical decision
making. However, poor imaging quality, such as non-uniform illumination or imbalanced …

The vessel-like structure in biomedical images, such as within cerebrovascular and nervous
pathologies, is an essential biomarker in understanding diseases' mechanisms and in …

Unpaired medical image enhancement (UMIE) aims to transform a low-quality (LQ) medical
image into a high-quality (HQ) one without relying on paired images for training. While most …

Distal symmetric polyneuropathy (DPN), particularly chronic sensorimotor DPN, represents
one of the most frequent complications of diabetes, affecting 50% of diabetic patients and …

Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in
image and video analysis. This review focuses on the application of AI in analyzing in vivo …

Background Optical coherence tomography angiography (OCTA) enables fast and non-
invasive high-resolution imaging of retinal microvasculature and is suggested as a potential …

Aims/hypothesis We aimed to develop an artificial intelligence (AI)-based deep learning
algorithm (DLA) applying attribution methods without image segmentation to corneal …

Background The retina and brain share a similar embryologic origin, blood barriers, and
microvasculature features. Thus, retinal imaging has been of interest in the aging population …

Automated extraction of cerebrovascular is of great importance in understanding the
mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However …

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data
annotation cost and model performance through employing sparsely-grained (ie, point-, box …