Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

A common shortfall of supervised deep learning for medical imaging is the lack of labeled
data, which is often expensive and time consuming to collect. This article presents a new …

In medical image analysis, it is typical to collect multiple annotations, each from a different
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …

Medical image segmentation is an important step in medical image analysis. With the rapid
development of a convolutional neural network in image processing, deep learning has …

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio
(CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the …

Background Most current algorithms for automatic glaucoma assessment using fundus
images rely on handcrafted features based on segmentation, which are affected by the …

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing
automatic screening methods first segment the main structure and subsequently calculate …

Segmentation is a fundamental task in medical image analysis. However, most existing
methods focus on primary region extraction and ignore edge information, which is useful for …

Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have
recently been proposed for automatic glaucoma detection based on fundus images …