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Liver Segmentation
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Cross Modality Medical Image Synthesis for Improving Liver Segmentation
A Reverse Mamba Attention Network for Pathological Liver Segmentation
Registration of Longitudinal Liver Examinations for Tumor Progress Assessment
ProKAN: Progressive Stacking of Kolmogorov-Arnold Networks for Efficient Liver Segmentation
SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing
Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs
Research on Tumors Segmentation based on Image Enhancement Method
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation
FourierLoss: Shape-Aware Loss Function with Fourier Descriptors
$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation
Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study
What can we learn about a generated image corrupting its latent representation?
Efficient liver segmentation with 3D CNN using computed tomography scans
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI
Decoupled Pyramid Correlation Network for Liver Tumor Segmentation from CT images
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
Transformer based Generative Adversarial Network for Liver Segmentation
Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
Deep Learning-based Assessment of Hepatic Steatosis on chest CT
RA V-Net: Deep learning network for automated liver segmentation
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data
EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT
Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow
Training on Polar Image Transformations Improves Biomedical Image Segmentation
Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning
Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision
Context-aware PolyUNet for Liver and Lesion Segmentation from Abdominal CT Images
Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration
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