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Found 7 papers, 0 papers with code
SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images
There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease.
Towards reliable calcification detection: calibration of uncertainty in coronary optical coherence tomography images
With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection.
Structural constrained virtual histology staining for human coronary imaging using deep learning
Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD).
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks
Network pruning is an effective method of model compression to handle such problems.
Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning
There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging.
Modeling and Validating Temporal Rules with Semantic Petri-Net for Digital Twins
Semantic rule checking on RDFS/OWL data has been widely used in the construction industry.
Machine-learning based three-qubit gate for realization of a Toffoli gate with cQED-based transmon systems
We use machine learning techniques to design a 50 ns three-qubit flux-tunable controlled-controlled-phase gate with fidelity of >99. 99% for nearest-neighbor coupled transmons in circuit quantum electrodynamics architectures.
