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

With the ever increasing demand for screening millions of prospective “novel coronavirus” or
COVID-19 cases, and due to the emergence of high false negatives in the commonly used …

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

Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of
patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) …

Accurate segmentation of brain tumor from magnetic resonance images (MRIs) is crucial for
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …

Deep learning (DL) models have provided state-of-the-art performance in various medical
imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) …

It is challenging to reduce the generalization errors of brain tumor segmentation models on
test data, as the nature of the high diversity of tumors. The model ensemble combining …

Objective Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life
expectancy. Physicians and oncologists urgently require automated techniques in clinics for …

As a highly malignant tumor, the incidence and mortality of glioma are not optimistic.
Predicting the survival time of patients with glioma by extracting the feature information from …

Automatic segmentation of glioma and its subregions is of great significance for diagnosis,
treatment and monitoring of disease. In this paper, an augmentation method, called …