Gliomas Disease Prediction: An Optimized Ensemble Machine Learning-Based Approach

Information Science, Computer Science, Artificial Intelligence, Medicine, Oncology,
brain tumor   glioma   machine learning   feature selection   prognosis   prediction   ensemble model  

Glioma Disease Prediction Based on Optimized Integrated Machine Learning Background and Research Objectives In medical research, gliomas are the most common type of primary brain tumors, encompassing various cancer types with different clinical behaviors and treatment outcomes. Accurate prognosis prediction for glioma patients is crucial for optimi...

Noninvasive Grading of Glioma by Knowledge Distillation Based Lightweight Convolutional Neural Network

Information Science, Artificial Intelligence, Medicine, Neurosurgery, Oncology,
knowledge distillation   glioma grading   convolutional neural network   lightweight model   image classification  

Review of Non-Invasive Glioma Grading Research: Lightweight Convolutional Neural Networks Based on Knowledge Distillation Background Gliomas are the main tumors of the central nervous system, and early detection is crucial. The World Health Organization (WHO) classifies gliomas from grade I to IV, with grades I and II being low-grade gliomas (LGG) ...

A Fully Automated Multimodal MRI-Based Multi-task Learning for Glioma Segmentation and IDH Genotyping

A Fully Automated Multimodal MRI-Based Multi-task Learning for Glioma Segmentation and IDH Genotyping

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Oncology, Life Sciences, Genetics,
multi-task learning   semi-supervised   glioma segmentation   IDH genotyping   transformer  

Research Report on Fully Automated Multimodal MRI Multi-task Learning for Glioma Segmentation and IDH Gene Typing Background of the Study Glioma is the most common primary brain tumor in the central nervous system. According to the World Health Organization (WHO) 2016 classification, gliomas are divided into low-grade gliomas (LGG, grades II and II...

An Attention-Guided CNN Framework for Segmentation and Grading of Glioma Using 3D MRI Scans

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Imaging, Oncology,
glioma   convolutional neural network   segmentation   magnetic resonance imaging   multi-task learning   attention mechanism  

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...

CaNet: Context Aware Network for Brain Glioma Segmentation

CaNet: Context Aware Network for Brain Glioma Segmentation

Information Science, Computer Science, Medicine, Neurology, Neurosurgery, Medical Imaging, Oncology, Life Sciences, Immunology,
brain glioma   conditional random field   graph convolutional network   image segmentation  

Context-Aware Network Study Report for Glioma Segmentation Glioma is a common type of adult brain tumor that severely harms health and has a high mortality rate. To provide sufficient evidence for early diagnosis, surgical planning, and postoperative observation, multimodal Magnetic Resonance Imaging (MRI) has been widely applied in this field. The...

Empowering Glioma Prognosis with Transparent Machine Learning and Interpretative Insights Using Explainable AI

Information Science, Computer Science, Artificial Intelligence, Medicine, Basic Medical Sciences, Neurology, Oncology,
glioma   molecular makeup   explainable artificial intelligence   SHAP   LIME   Qlattice   ELI5   machine learning  

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...