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 361–370 of 436 papers

TitleStatusHype
Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation ProblemCode0
Organ At Risk Segmentation with Multiple Modality—0
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Brain MRI Tumor Segmentation with Adversarial Networks—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Segmentation and Survival Prediction—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
Show:102550
← PrevPage 37 of 44Next →

No leaderboard results yet.