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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 301–350 of 436 papers

TitleStatusHype
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
Belief function-based semi-supervised learning for brain tumor segmentation—0
Towards annotation-efficient segmentation via image-to-image translation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
Brain Tumor Detection Based On Symmetry Information—0
Brain Tumor Segmentation: A Comparative Analysis—0
Brain Tumor Segmentation and Survival Prediction—0
Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer—0
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
Brain Tumor Segmentation on MRI with Missing Modalities—0
Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation—0
Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features—0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Brain tumor segmentation with missing modalities via latent multi-source correlation representation—0
Brain Tumor Survival Prediction using Radiomics Features—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
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
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
Decentralized Differentially Private Segmentation with PATE—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation—0
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