SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–210 of 436 papers

TitleStatusHype
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
Federated brain tumor segmentation: an extensive benchmark—0
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI—0
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Show:102550
← PrevPage 21 of 44Next →

No leaderboard results yet.