Light-sheet fluorescence microscopy has transformed our ability to visualize and
quantitatively measure biological processes rapidly and over long time periods. In this …

Recent developments in deep learning have enabled data-driven algorithms that can reach
human-level performance and beyond. The development and deployment of medical image …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Skin diseases are one of the most common ailments affecting humans. Artificial intelligence
based on deep learning can significantly improve the efficiency of identifying skin disorders …

Deep neural networks (DNNs) have been widely applied in the medical image community,
contributing to automatic ophthalmic screening systems for some common diseases …

Vision Transformer (ViT) is emerging as a new leader in computer vision with its outstanding
performance in many tasks (eg, ImageNet-22k, JFT-300M). However, the success of ViT …

Medical image segmentation has seen significant progress through the use of supervised
deep learning. Hereby, large annotated datasets were employed to reliably segment …

Stroke, particularly ischemic stroke, is a major cause of disability and one of the leading
causes of adult mortality worldwide. Early and prompt management of stroke patients can …