Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …

Recent advances in machine learning and prevalence of digital medical images have
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …

Background Image segmentation is an essential step in the analysis and subsequent
characterisation of brain tumours through magnetic resonance imaging. In the literature …

Automatic brain tumor segmentation from multimodal MRI plays a significant role in assisting
the diagnosis, treatment, and surgery of glioblastoma and lower glade glioma. In this article …

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have
demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung …

Glioma is the deadliest brain tumor with high mortality. Treatment planning by human
experts depends on the proper diagnosis of physical symptoms along with Magnetic …

Accurate brain tumor segmentation plays a pivotal role in clinical practice and research
settings. In this paper, we propose the multi-level up-sampling network (MU-Net) to learn the …

Semi-supervised learning has attracted wide attention from many researchers since its
ability to utilize a few data with labels and relatively more data without labels to learn …

Although the U-shaped architecture, represented by UNet, has become a major network
model for brain tumor segmentation, the repeated convolution and sampling operations can …

Neurological disorders are disabilities of the growth and development of the brain or central
nervous system. Cancer-related neurological disorders are frequent among brain tumor …