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 161–170 of 436 papers

TitleStatusHype
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
Autofocus Layer for Semantic SegmentationCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsCode0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion SegmentationCode0
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
Volumetric medical image segmentation through dual self-distillation in U-shaped networksCode0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
CNN-based Segmentation of Medical Imaging DataCode0
Show:102550
← PrevPage 17 of 44Next →

No leaderboard results yet.