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

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …

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

Over the past decade, convolutional neural networks (CNN) have shown very competitive
performance in medical image analysis tasks, such as disease classification, tumor …

Multi-modality is widely used in medical imaging, because it can provide multiinformation
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …

Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …

Although having achieved great success in medical image segmentation, deep
convolutional neural networks usually require a large dataset with manual annotations for …

Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …

Semi-supervised learning has a great potential in medical image segmentation tasks with a
few labeled data, but most of them only consider single-modal data. The excellent …