Graph Convolutional Network Based on Self-Attention Similarity for Multi-type Low-Grade Glioma Classification 1. Research Background Low-grade glioma is a common malignant brain tumor caused by the cancerous transformation of glial cells in the brain and spinal cord. Gliomas are characterized by high incidence, high recurrence rate, high mortality ...
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...
Application of Multi-modal Disentangled Variational Autoencoder and Game Theory Interpretability in Glioma Grading Background Gliomas are the most common primary brain tumors in the central nervous system. According to cellular activity and invasiveness, the World Health Organization (WHO) classifies them into grades I to IV, with grades I and II r...
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...
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...
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...
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...
Report on the Study of Fluorescence Molecular Tomography for Morphological Reconstruction of Glioma Based on Group Sparsity Priors 1. Academic Background and Research Motivation Fluorescence Molecular Tomography (FMT) is an important tool in life sciences that allows non-invasive real-time three-dimensional (3D) visualization of fluorescence source...
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...
Globally, the most common and deadly malignant brain tumor is glioblastoma (Glioblastoma, GBM). In recent years, research has continuously attempted to predict the overall survival time (OS) of GBM patients using machine learning techniques based on preoperative single-modality or multi-modality imaging phenotypes. Although these machine learning m...