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Cardiac Segmentation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MISSFormer: An Effective Medical Image Segmentation Transformer
Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation
Source-Free Domain Adaptation for Image Segmentation
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
Trilateral Attention Network for Real-time Medical Image Segmentation
Unsupervised Domain Adaptation with Variational Approximation for Cardiac Segmentation
Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions
Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation
Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation
Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework
Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation
Style-invariant Cardiac Image Segmentation with Test-time Augmentation
Disentangled Representations for Domain-generalized Cardiac Segmentation
Learning Directional Feature Maps for Cardiac MRI Segmentation
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-efficient Cardiac Segmentation
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
Cardiac Segmentation with Strong Anatomical Guarantees
On the effectiveness of GAN generated cardiac MRIs for segmentation
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI
CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Disentangle, align and fuse for multimodal and semi-supervised image segmentation
Cardiac Segmentation of LGE MRI with Noisy Labels
An Automatic Cardiac Segmentation Framework based on Multi-sequence MR Image
Combining Multi-Sequence and Synthetic Images for Improved Segmentation of Late Gadolinium Enhancement Cardiac MRI
Discretely-constrained deep network for weakly supervised segmentation
Cardiac MRI Segmentation with Strong Anatomical Guarantees
Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
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