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 391–400 of 436 papers

TitleStatusHype
Deep Learning with Mixed Supervision for Brain Tumor Segmentation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
A Pretrained DenseNet Encoder for Brain Tumor Segmentation—0
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS ChallengeCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
Show:102550
← PrevPage 40 of 44Next →

No leaderboard results yet.