The transformer is primarily used in the field of natural language processing. Recently, it has
been adopted and shows promise in the computer vision (CV) field. Medical image analysis …

Brain tumors have shown extreme mortality and increasing incidence during recent years,
which bring enormous challenges for the timely diagnosis and effective treatment of brain …

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain
tumor diagnosis, cancer management and research purposes. With the great success of the …

Abstract 3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and
treatment. Accurate segmentation results are critical for localization and spatial distribution …

Accurate segmentation of brain tumors in MRI images is imperative for precise clinical
diagnosis and treatment. However, existing medical image segmentation methods exhibit …

Deep learning methods are frequently used in segmenting histopathology images with high-
quality annotations nowadays. Compared with well-annotated data, coarse, scribbling-like …

In this review, we compiled convolutional neural network (CNN) methods which have the
potential to automate the manual, costly and error-prone processing of medical images. We …

The model, Transformer, is known to rely on a self-attention mechanism to model distant
dependencies, which focuses on modeling the dependencies of the global elements …

Brain tumor segmentation is a fundamental task and existing approaches usually rely on
multi-modality magnetic resonance imaging (MRI) images for accurate segmentation …

Brain tumor segmentation is an important direction in medical image processing, and its
main goal is to accurately mark the tumor part in brain MRI. This study proposes a brand …