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 8190 of 436 papers

TitleStatusHype
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor SegmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and PediatricsCode1
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
3D Self-Supervised Methods for Medical ImagingCode1
DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR ImagesCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
What is the best data augmentation for 3D brain tumor segmentation?Code1
Show:102550
← PrevPage 9 of 44Next →

No leaderboard results yet.