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 231240 of 436 papers

TitleStatusHype
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI0
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification0
Topology-Aware Focal Loss for 3D Image Segmentation0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations0
Brain MRI Tumor Segmentation with Adversarial Networks0
Transfer learning for automatic brain tumor classification Using MRI Images.0
Transfer Learning for Brain Tumor Segmentation0
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review0
Show:102550
← PrevPage 24 of 44Next →

No leaderboard results yet.