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 181–190 of 436 papers

TitleStatusHype
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor SegmentationCode0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction—0
Show:102550
← PrevPage 19 of 44Next →

No leaderboard results yet.