Atrous convolution and attention have improved the performance of the UNet architecture for
segmentation purposes. However, a perfect combination of atrous convolution and attention …

Deep supervision, or known as' intermediate supervision'or'auxiliary supervision', is to add
supervision at hidden layers of a neural network. This technique has been increasingly …

The number of brain tumor cases has increased in recent years. Therefore, accurate
diagnosis and treatment of brain tumors are extremely important. Accurate detection of tumor …

Gliomas are the most common and aggressive form of all brain tumors, with a median
survival rate of fewer than two years, especially for the highest-grade glioma patient …

Automated segmentation methods can produce faster segmentation of tumors in medical
images, aiding medical professionals in diagnosis and treatment plans. A 3D U-Net method …

Brain tumor segmentation is an essential task for medical diagnosis and treatment planning.
Multi-modal MRI provides complementary information that is essential for accurate …

Abstract 3D UNet has achieved high brain tumor segmentation performance but requires
high computation, large memory, abundant training data, and has limited interpretability. As …

Brain tumor segmentation in multimodal MRI volumes is of great significance to disease
diagnosis, treatment planning, survival prediction and other relevant tasks. However, most …

Gliomas are the most common primary malignant tumors of the brain. Magnetic resonance
(MR) imaging is one of the main detection methods of brain tumors, so accurate …

An important challenge and limiting factor in deep learning methods for medical imaging
segmentation is the lack of available of annotated data to properly train models. For the …