Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40038981
Due to its complexity and time-consuming nature, identifying gliomas at the Magnetic Resonance Imaging (MRI) slice-level before segmentation could assist clinicians in minimizing the time required for this procedure. In the literature, many studies p...
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...
Preoperative classification of brain tumors is critical to developing personalized treatment plans, however existing classification methods rely on manual intervention and often have problems with efficiency and accuracy, which may lead to misdiagnos...
Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain's functioning prominently. However,...
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....
Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especially in regions with limited access to medical expertise. However, existing methods often overlook edge pixel information during tumor segmentation, l...
High-grade glioma (HGG) is an aggressive brain tumor with poor survival rates. Predicting survival outcomes is critical for personalized treatment planning. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep le...
PURPOSE: To develop a fully AI-based dose estimation model capable of learning and estimating single pencil beam dose distributions, and to verify its performance by testing the model's generalizability on unseen, previously delivered treatment plans...
BACKGROUND: Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prognostication and management strategy. Recent advances in artificial intelligence (AI) have resulted i...
We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene...