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Brain Tumor Segmentation
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Brain Tumor Segmentation with Deep Neural Networks )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities
VIViT: Variable-Input Vision Transformer Framework for 3D MR Image Segmentation
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion
Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)
Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalities
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation
PSO-UNet: Particle Swarm-Optimized U-Net Framework for Precise Multimodal Brain Tumor Segmentation
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor Segmentation
Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI Data
Clinical Inspired MRI Lesion Segmentation
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation
Position Paper: Building Trust in Synthetic Data for Clinical AI
Deep Ensemble approach for Enhancing Brain Tumor Segmentation in Resource-Limited Settings
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentation
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learning
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan Africa
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation
Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors
QCResUNet: Joint Subject-level and Voxel-level Segmentation Quality Prediction
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