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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 101–150 of 436 papers

TitleStatusHype
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor SegmentationCode1
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image SegmentationCode1
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor SegmentationCode0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Self-calibrated convolution towards glioma segmentation—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessmentCode0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Hybrid-Fusion Transformer for Multisequence MRICode0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
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
Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image SegmentationCode1
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
AME-CAM: Attentive Multiple-Exit CAM for Weakly Supervised Segmentation on MRI Brain TumorCode1
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
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
Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelsCode1
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI—0
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