Study of Attention-Guided CNN Framework for 3D MRI Glioma Segmentation and Grading Gliomas are the most deadly form of brain tumors in humans. Timely diagnosis of these tumors is a crucial step for effective tumor treatment. Magnetic Resonance Imaging (MRI) typically provides a non-invasive examination of brain lesions. However, manual inspection o...

Enabling Explainable Artificial Intelligence for Glioma Prognosis: Translational Insights from Transparent Machine Learning Academic Background This study is dedicated to developing a reliable technique to detect whether patients have a specific type of brain tumor—glioma—using various machine learning methods and deep learning methods, combined wi...

Survival Analysis of Glioblastoma Patients: An Overview Empowered by Data Engineering Introduction Glioblastoma is a type of tumor that occurs in glial cells and accounts for 26.7% of all primary brain and central nervous system tumors. Survival analysis of glioblastoma patients is a key task in clinical management due to the heterogeneity of the t...

Personalized Prediction of Glioma Growth Using Bayesian Inference Introduction Glioblastoma is the most aggressive type of primary brain tumor, characterized by highly invasive tumor cells that spread to surrounding tissues. Conventional medical imaging techniques cannot precisely identify these diffuse tumor boundaries, leading to suboptimal clini...

Study of Mathematical Models for Tumor Growth – Exploring Glioma Extension Using Longitudinal Magnetic Resonance Imaging A recent article published in the IEEE Transactions on Biomedical Engineering presents a systematic study on the mathematical modeling and growth patterns of gliomas (glioma). This research was conducted by Birkan Tunç, David A. ...