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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.
Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentation
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset
Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs
SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments
Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation
XBound-Former: Toward Cross-scale Boundary Modeling in Transformers
A Survey on Deep Learning for Skin Lesion Segmentation
Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images
Class Balanced PixelNet for Neurological Image Segmentation
Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition
Ischemic Stroke Lesion Segmentation Using Adversarial Learning
Segmentation-Consistent Probabilistic Lesion Counting
CCAT-NET: A Novel Transformer Based Semi-supervised Framework for Covid-19 Lung Lesion Segmentation
MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation
ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities
Constrained unsupervised anomaly segmentation
AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation
A novel unsupervised covid lung lesion segmentation based on the lung tissue identification
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks
A Field of Experts Prior for Adapting Neural Networks at Test Time
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
Joint Liver and Hepatic Lesion Segmentation in MRI using a Hybrid CNN with Transformer Layers
RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation
Cortical lesions, central vein sign, and paramagnetic rim lesions in multiple sclerosis: emerging machine learning techniques and future avenues
A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19
Expert Knowledge-guided Geometric Representation Learning for Magnetic Resonance Imaging-based Glioma Grading
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