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Tumor Segmentation
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Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.
논문
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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
Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images
Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention
BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
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
PCA for Enhanced Cross-Dataset Generalizability in Breast Ultrasound Tumor Segmentation
Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalities
TAGS: 3D Tumor-Adaptive Guidance for SAM
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
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models
Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing
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
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation
Here Comes the Explanation: A Shapley Perspective on Multi-contrast Medical Image Segmentation
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining
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
Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images
Selective Complementary Feature Fusion and Modal Feature Compression Interaction for Brain Tumor Segmentation
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts
SurgicalVLM-Agent: Towards an Interactive AI Co-Pilot for Pituitary Surgery
Rel-UNet: Reliable Tumor Segmentation via Uncertainty Quantification in nnU-Net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.