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Lesion Segmentation
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Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
<span style="color:grey; opacity: 0.6">( Image credit: D-UNet )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Hybrid Attention Network for Accurate Breast Tumor Segmentation in Ultrasound Images
Integrating Radiomics with Deep Learning Enhances Multiple Sclerosis Lesion Delineation
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework
How We Won the ISLES'24 Challenge by Preprocessing
HWA-UNETR: Hierarchical Window Aggregate UNETR for 3D Multimodal Gastric Lesion Segmentation
BrainSegDMlF: A Dynamic Fusion-enhanced SAM for Brain Lesion Segmentation
GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation
Lesion-Aware Generative Artificial Intelligence for Virtual Contrast-Enhanced Mammography in Breast Cancer
OmniMamba4D: Spatio-temporal Mamba for longitudinal CT lesion segmentation
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation
Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors
Multi-Granularity Vision Fastformer with Fusion Mechanism for Skin Lesion Segmentation
STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object Segmentation with Zero-Shot Method
Modality-Independent Brain Lesion Segmentation with Privacy-aware Continual Learning
Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation
ASP-VMUNet: Atrous Shifted Parallel Vision Mamba U-Net for Skin Lesion Segmentation
Learning to segment anatomy and lesions from disparately labeled sources in brain MRI
Self-Attention Diffusion Models for Zero-Shot Biomedical Image Segmentation: Unlocking New Frontiers in Medical Imaging
An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation
Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance
Minding Fuzzy Regions: A Data-driven Alternating Learning Paradigm for Stable Lesion Segmentation
State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters
Interactive Segmentation and Report Generation for CT Images
ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation
GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis
Clinical Inspired MRI Lesion Segmentation
EyeBench: A Call for More Rigorous Evaluation of Retinal Image Enhancement
Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms
Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of Loss Functions
DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation
A Data-Efficient Pan-Tumor Foundation Model for Oncology CT Interpretation
Generalizable automated ischaemic stroke lesion segmentation with vision transformers
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