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 121–130 of 436 papers

TitleStatusHype
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—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
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
Show:102550
← PrevPage 13 of 44Next →

No leaderboard results yet.