Stacked BCDU-net with semantic CMR synthesis: application to Myocardial PathologySegmentation challenge
EfficientSeg: A Simple but Efficient Solution to Myocardial Pathology Segmentation Challenge
Two-stage Method for Segmentation of the Myocardial Scars and Edema on Multi-sequence Cardiac Magnetic Resonance
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
Myocardial Edema and Scar Segmentation using a Coarse-to-Fine Framework with Weighted Ensemble
Exploring ensemble applications for multi-sequence myocardial pathology segmentation
Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention and Dynamic Resampling
Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences
CMS-UNet: Cardiac Multi-task Segmentation in MRI with a U-shaped Network
Automatic Myocardial Scar Segmentation from Multi-Sequence Cardiac MRI using Fully Convolutional Densenet with Inception and Squeeze-Excitation Module
Dual Attention U-net for Multi-Sequence Cardiac MR Images Segmentation
Accurate Myocardial Pathology Segmentation with Residual U-Net
Stacked and Parallel U-Nets with Multi-Output for Myocardial Pathology Segmentation
Dual-path Feature Aggregation Network Combined Multi-layer Fusion for Myocardial Pathology Segmentation with Multi-sequence Cardiac MR
Cascaded Framework with Complementary CMR Information for Myocardial Pathology Segmentation
CMRadjustNet: Recognition and standardization of cardiac MRI orientation via multi-tasking learning and deep neural networks.