Abstract Purpose Convolutional Neural Networks (ConvNets) have quickly become popular
machine learning techniques in recent years, particularly in the classification and …

Background Accurate and non-invasive estimation of MGMT promoter methylation status in
glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive …

Glioblastoma is a type of cancer known for its fast growth, invasive behavior, and resistance
to different treatments. It accounts for more than 50% of all malignant brain tumors. Due to its …

Abstract Representation learning aims to extract meaningful features from medical images
that are often multimodal, ie, captured using multiple imaging modalities, to provide a more …

Ovarian cancer is a formidable health challenge that demands accurate and timely survival
predictions to guide clinical interventions. Existing methods, while commendable, suffer from …

Radiomics and radiogenomics have recently proved advantageous in the healthcare sector,
especially in cancer treatment. There has been a surge in the use of various artificial …

Objective: Glioblastoma multiforme (GBM), particularly the IDH-wildtype type, represents a
significant clinical challenge due to its aggressive nature and poor prognosis. Despite …

O-6-methylguanine-DNA methyltransferase (MGMT), a notable gene promoter, is strongly
related with the treatment effectiveness in glioblastoma (GBM). Analyzing tumor tissues is …

Glioblastoma is the predominant and highly malignant brain tumor detected in the adult
population. The observed phenomenon demonstrates a notable level of genetic variation, so …

Abstract Machine learning approaches have emerged as powerful tools for epilepsy
analysis in current clinical trials, revolutionizing the field of epilepsy research and patient …