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 241–250 of 436 papers

TitleStatusHype
Organ At Risk Segmentation with Multiple Modality—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Prediction of Overall Survival of Brain Tumor Patients—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
Show:102550
← PrevPage 25 of 44Next →

No leaderboard results yet.