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 321–330 of 436 papers

TitleStatusHype
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Brain Tumor Survival Prediction using Radiomics Features—0
Spherical coordinates transformation pre-processing in Deep Convolution Neural Networks for brain tumor segmentation in MRI—0
Abstracting Deep Neural Networks into Concept Graphs for Concept Level InterpretabilityCode1
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation—0
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
CANet: Context Aware Network for 3D Brain Glioma SegmentationCode1
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restorationCode1
Multi-Domain Image Completion for Random Missing Input Data—0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
Show:102550
← PrevPage 33 of 44Next →

No leaderboard results yet.