Improved Segmentation of Pediatric Low-Grade Gliomas Through Multitask Learning Background Introduction The segmentation of pediatric brain tumors is a critical task in tumor volume analysis and artificial intelligence algorithms. However, this process is time-consuming and requires the expertise of neuroradiologists. Although significant research ...
“Prediction of Glioma Grades Based on Radiomic Features Inside and Outside Tumors Using Multiparametric MRI Images” Research Background Glioma is the most common primary brain tumor in the central nervous system, accounting for 80% of adult malignant brain tumors. In clinical practice, treatment decisions often require individualized adjustments ba...
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 ...
AI-Assisted Radiomics Algorithm for Glioma Grading Based on Slice Pooling Background Introduction Glioma is the most common and threatening tumor in the central nervous system, characterized by high incidence, high recurrence rates, high mortality, and low cure rates. The World Health Organization (WHO) classifies gliomas into four grades (I, II, I...
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...
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) ...
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...