Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain
disease and monitor treatment as non-invasive imaging technology. MRI produces three …

Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …

In this paper, we devise a novel two-stage cascaded U-Net to segment the substructures of
brain tumors from coarse to fine. The network is trained end-to-end on the Multimodal Brain …

Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …

Identifying the tumour's extent is a major challenge in planning treatment for brain tumours
and correctly measuring their size. Magnetic resonance imaging (MRI) has emerged as a …

Most existing brain tumor segmentation methods usually exploit multi-modal magnetic
resonance imaging (MRI) images to achieve high segmentation performance. However, the …

Class imbalance has emerged as one of the major challenges for medical image
segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates …

We present an adversarial domain adaptation based deep learning approach for automatic
tumor segmentation from T2-weighted MRI. Our approach is composed of two steps:(i) a …

The semantic segmentation of a brain tumor is of paramount importance for its treatment and
prevention. Recently, researches have proposed various neural network-based …

Deep Learning (DL) algorithms enabled computational models consist of multiple
processing layers that represent data with multiple levels of abstraction. In recent years …