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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

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Showing 201–250 of 436 papers

TitleStatusHype
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet—0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
Cascaded V-Net using ROI masks for brain tumor segmentation—0
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation—0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
Clinical Inspired MRI Lesion Segmentation—0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation—0
Conditional generator and multi-sourcecorrelation guided brain tumor segmentation with missing MR modalities—0
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
Context Aware 3D UNet for Brain Tumor Segmentation—0
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation—0
DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation—0
Modality-Pairing Learning for Brain Tumor Segmentation—0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Multi-Domain Image Completion for Random Missing Input Data—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
Multimodal Self-Supervised Learning for Medical Image Analysis—0
Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction—0
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation—0
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data—0
Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans—0
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Organ At Risk Segmentation with Multiple Modality—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
Position Paper: Building Trust in Synthetic Data for Clinical AI—0
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
Prediction of Overall Survival of Brain Tumor Patients—0
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors—0
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation—0
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation—0
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