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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 376–400 of 436 papers

TitleStatusHype
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality PredictionCode0
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor SegmentationCode0
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional NetworksCode0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scansCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
Brain tumour segmentation using a triplanar ensemble of U-NetsCode0
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor SegmentationCode0
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRICode0
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation NetworkCode0
SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor SegmentationCode0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion SegmentationCode0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
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