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 251–300 of 436 papers

TitleStatusHype
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial TrainingCode0
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Optimized U-Net for Brain Tumor SegmentationCode0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image SegmentationCode0
Self-supervised Tumor Segmentation through Layer Decomposition—0
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation—0
Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation—0
3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma SegmentationCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
HMM Model for Brain Tumor Detection and ClassificationCode0
Knowledge distillation from multi-modal to mono-modal segmentation networks—0
Show:102550
← PrevPage 6 of 9Next →

No leaderboard results yet.