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Brain Tumor Segmentation

Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.

( Image credit: Brain Tumor Segmentation with Deep Neural Networks )

Papers

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Showing 291–300 of 436 papers

TitleStatusHype
A Structural Graph-Based Method for MRI Analysis—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
Automated ensemble method for pediatric brain tumor segmentation—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
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