In the global context, the prevalence of obesity is on the rise, bringing significant impacts to public health. Obesity is independently associated with the incidence and mortality of cardiovascular diseases, with an estimated economic burden exceeding $200 billion annually for healthcare systems. In recent years, glucagon-like peptide-1 (GLP-1) re...
Characterization of Heterogeneity in Muscle Fiber Types and Selective Susceptibility in Inclusion Body Myositis With advancing age, the incidence of inflammatory myopathies gradually increases, among which inclusion body myositis (IBM) is the most common type, currently lacking effective treatment methods. Unlike other inflammatory myopathies, IBM ...
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...