Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

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

Survival analysis is a critical task in glioma patient management due to the inter and intra
tumor heterogeneity. In clinical practice, clinicians estimate the survival with their …

Abstract Pathology Artificial Intelligence Platform (PAIP) is a free research platform in
support of pathological artificial intelligence (AI). The main goal of the platform is to construct …

An artificial intelligence (AI) model's performance is strongly influenced by the input features.
Therefore, it is vital to find the optimal feature set. It is more crucial for the survival prediction …

The paper demonstrates the use of the fully convolutional neural network for glioma
segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture …

The accurate prognosis of glioblastoma multiforme (GBM) plays an essential role in
planning correlated surgeries and treatments. The conventional models of survival …

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

Radiomics-guided prediction of overall survival (OS) in brain gliomas is seen as a significant
problem in Neuro-oncology. The ultimate goal is to develop a robust MRI-based approach …

The most common and aggressive malignant brain tumor in adults is glioma, which leads to
short life expectancy. A reliable and efficient automatic segmentation method is beneficial for …