Introduction: Accurate diagnosis is crucial for brain tumors, given their low survival rates and
high treatment costs. However, traditional methods relying on manual interpretation of …

Gliomas are the most common and aggressive form of all brain tumors, with medial survival
rates of less than two years for the highest grade. While accurate and reproducible …

Vision Transformers have recently shown impressive performances on medical image
segmentation. Despite their strong capability of modeling long-range dependencies, the …

When it comes to brain tumors, there's no other disease that has as heavy an impact on life
expectancy, and not only is it among the main causes of death globally. The only way out of …

Semi-supervised learning (SSL), as one of the dominant methods, aims at leveraging the
unlabeled data to deal with the annotation dilemma of supervised learning, which has …

Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning
and monitoring. Traditional methods often encounter challenges due to the complexity and …

For multi-modal magnetic resonance (MR) brain tumor image segmentation, current
methods usually directly extract the discriminative features from input images for tumor sub …

Abstract Background and Objective. Skin lesion classification by using deep learning
technologies is still a considerable challenge due to high similarity among classes and large …

Medical image segmentation is crucial for obtaining accurate diagnoses, and while
convolutional neural network (CNN)-based methods have made strides in recent years, they …

Objective. Due to non-invasive imaging and the multimodality of magnetic resonance
imaging (MRI) images, MRI-based multi-modal brain tumor segmentation (MBTS) studies …