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...
“IEEE Transactions on Biomedical Engineering” August 2022, Vol. 69, No. 8, First Clinical Study: Application of Near-Infrared Window IIA/IIB Fluorescence Imaging in Precise Glioma Resection Surgery Cao Caiguang, Jin Zeping, Shi Xiaojing, Zhang Zhe, Xiao Anqi, Yang Junying, Ji Nan, Tian Jie (IEEE Member), Hu Zhenhua (IEEE Senior Member) Introduction...
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...
Numerical Analysis of Rectangular Open-Channel PCF Biosensor Embedded with TiO2-Au-MXene for Brain Tumor Diagnosis Academic Background and Problem Statement In recent years, the development of cost-effective and highly reliable biosensors has become a research hotspot. These sensors aim to detect minute concentrations of analytes and cover a wide a...
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...