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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
Segment Anything Model for Brain Tumor Segmentation—0
Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars—0
DSFNet: Dual-GCN and Location-fused Self-attention with Weighted Fast Normalized Fusion for Polyps SegmentationCode0
Automated ensemble method for pediatric brain tumor segmentation—0
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data—0
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation—0
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Confidence Intervals for Performance Estimates in Brain MRI Segmentation—0
A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation—0
Source Identification: A Self-Supervision Task for Dense Prediction—0
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing TasksCode0
A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor SegmentationCode0
Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation—0
Volumetric medical image segmentation through dual self-distillation in U-shaped networksCode0
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)—0
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning—0
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Medical Image Analysis using Deep Relational Learning—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient SupervisionCode0
Investigating certain choices of CNN configurations for brain lesion segmentation—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
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