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

Showing 1120 of 436 papers

TitleStatusHype
Rethinking Brain Tumor Segmentation from the Frequency Domain PerspectiveCode1
Brain Tumor Segmentation and Survival Prediction using 3D Attention UNetCode1
3D Self-Supervised Methods for Medical ImagingCode1
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Attention U-Net: Learning Where to Look for the PancreasCode1
Attention-Guided Version of 2D UNet for Automatic Brain Tumor SegmentationCode1
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Show:102550
← PrevPage 2 of 44Next →

No leaderboard results yet.