Papers with Code 아카이브
Cardiac Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networks
GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic Features for Medical Image Segmentation
Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation
SFB-net for cardiac segmentation: Bridging the semantic gap with attention
RotCAtt-TransUNet++: Novel Deep Neural Network for Sophisticated Cardiac Segmentation
How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation
ModelMix: A New Model-Mixup Strategy to Minimize Vicinal Risk across Tasks for Few-scribble based Cardiac Segmentation
CAMS: Convolution and Attention-Free Mamba-based Cardiac Image Segmentation
Direct Cardiac Segmentation from Undersampled K-space Using Transformers
LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation
TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentation
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions
SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation
Towards Robust Cardiac Segmentation using Graph Convolutional Networks
NISF: Neural Implicit Segmentation Functions
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizability
Unsupervised Cardiac Segmentation Utilizing Synthesized Images from Anatomical Labels
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts
Self-Supervised Pretraining for 2D Medical Image Segmentation
DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via A Structure-Specific Generative Method
ShapePU: A New PU Learning Framework Regularized by Global Consistency for Scribble Supervised Cardiac Segmentation
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
Test-Time Adaptation with Shape Moments for Image Segmentation
Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.