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Medical Image Segmentation
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Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.
<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DiffOSeg: Omni Medical Image Segmentation via Multi-Expert Collaboration Diffusion Model
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
Just Say Better or Worse: A Human-AI Collaborative Framework for Medical Image Segmentation Without Manual Annotations
Causal-SAM-LLM: Large Language Models as Causal Reasoners for Robust Medical Segmentation
SAMed-2: Selective Memory Enhanced Medical Segment Anything Model
Autoadaptive Medical Segment Anything Model
MedSAM-CA: A CNN-Augmented ViT with Attention-Enhanced Multi-Scale Fusion for Medical Image Segmentation
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model
MedPrompt: LLM-CNN Fusion with Weight Routing for Medical Image Segmentation and Classification
GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models
EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis
AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Weighted Mean Frequencies: a handcraft Fourier feature for 4D Flow MRI segmentation
AI-Driven MRI-based Brain Tumour Segmentation Benchmarking
SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting
Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention
SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus
MedSeg-R: Medical Image Segmentation with Clinical Reasoning
Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster
TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration
Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
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
Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation
Pixel-wise Modulated Dice Loss for Medical Image Segmentation
Simple is what you need for efficient and accurate medical image segmentation
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
SWDL: Stratum-Wise Difference Learning with Deep Laplacian Pyramid for Semi-Supervised 3D Intracranial Hemorrhage Segmentation
MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models
ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation
Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches
Med-URWKV: Pure RWKV With ImageNet Pre-training For Medical Image Segmentation
SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.