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Found 6 papers, 2 papers with code
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Manual analysis of emphysema subtypes and severity is laborious and subjective.
Structure and position-aware graph neural network for airway labeling
The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks (CNN) and enriched using graph neural networks.
Deep Clustering Activation Maps for Emphysema Subtyping
We propose a deep learning clustering method that exploits dense features from a segmentation network for emphysema subtyping from computed tomography (CT) scans.
Dense Regression Activation Maps For Lesion Segmentation in CT scans of COVID-19 patients
Therefore, we propose a weakly-supervised segmentation method based on dense regression activation maps (dRAMs).
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD.
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
Such performance gain is more pronounced with transfer learning or in the case of limited training data.
