IEEE

Lightweight 3D IDC-MambaUNet for Multi-Modal MRI Brain Tumor Segmentation Using Multi-Resolution Ensemble

Abstract: Brain tumor segmentation on MRI is a critical task in clinical diagnosis and treatment planning. To address this issue, we propose a lightweight network based on the U-Net architecture—3D ...
GitHub

Attention-Guided Vector Quantized Variational Autoencoder for Brain Tumor Segmentation

Additional visualizations highlighting the comparison between the proposed two-stage AG-VQ-VAE network (without skip connections) and the single-stage AG-UNet (with skip connections) are presented.
Frontiers

Comparative analysis of machine learning techniques on the BraTS dataset for brain tumor classification

1 Information Statistics Center, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China 2 School of Computer Science and Technology, Hubei Business ...
IEEE

HMSA-Net: A Hierarchical Multi-Scale Attention Network for Brain Tumor Segmentation from Multi-Modal MRI

Abstract: This study introduces HMSA-Net, a Hierarchical Multi-Scale Attention Network designed for accurate brain tumor segmentation from multi-modal MRI scans. HMSA-Net employs a hierarchical ...
Medical Xpress

Prolonged use of injectable contraceptive tied to brain tumor diagnoses

From Case Western Reserve University School of Medicine and Cleveland Clinic, investigators report that depot medroxyprogesterone acetate use was associated with a higher relative risk of meningioma ...