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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 201–210 of 436 papers

TitleStatusHype
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
A Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Brain Tumor Segmentation on MRI with Missing Modalities—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing—0
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
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