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 111–120 of 436 papers

TitleStatusHype
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Brain Tumor Segmentation on MRI with Missing Modalities—0
Brain Tumor Survival Prediction using Radiomics Features—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer—0
Show:102550
← PrevPage 12 of 44Next →

No leaderboard results yet.