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 …

Automatic segmentation of brain tumor regions from multimodal MRI scans is of great clinical
significance. In this letter, we propose a “Segmentation-Fusion” multi-task model named SF …

Abstract Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate
brain tumor segmentation. The main problem is that not all types of MRIs are always …

Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and
evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely …

The deep learning method has shown its outstanding performance in object recognition and
becomes the first choice for medical image analysis. However, how to effectively propagate …

Accurate medical image segmentation commonly requires effective learning of the
complementary information from multimodal data. However, in clinical practice, we often …

For medical imaging tasks, it is a prevalent practice to have a multi-modality image dataset,
as experts prefer using multiple medical devices to diagnose a disease. Each device can …

Medical practitioners generally rely on multimodal brain images, for example based on the
information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis …

The manual brain tumor annotation process is time consuming and resource consuming,
therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In …

Accurate medical image segmentation of brain tumors is necessary for the diagnosing,
monitoring, and treating disease. In recent years, with the gradual emergence of multi …