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Found 6 papers, 3 papers with code
MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography
The COVID-19 pandemic response highlighted the potential of deep learning methods in facilitating the diagnosis, prognosis and understanding of lung diseases through automated segmentation of pulmonary structures and lesions in chest computed tomography (CT).
Automatic segmentation of lung findings in CT and application to Long COVID
A comprehensive ablation study was performed to evaluate the contribution of the proposed network modifications.
LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation
To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects.
Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network
Once trained, the framework can be used as a registration-free method for predicting local tissue expansion.
Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion Artifacts
Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression.
CT Image Segmentation for Inflamed and Fibrotic Lungs Using a Multi-Resolution Convolutional Neural Network
Regional lobar analysis was performed using hierarchical clustering to identify radiographic subtypes of COVID-19.
