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Found 9 papers, 0 papers with code
Interpretable and Efficient Beamforming-Based Deep Learning for Single Snapshot DOA Estimation
We introduce an interpretable deep learning approach for direction of arrival (DOA) estimation with a single snapshot.
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
One key advantage of our model is its ability to train deep networks using SAM-generated pseudo labels without relying on a set of expert-level annotations while attaining good segmentation performance.
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.
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology.
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).
Automated player identification and indexing using two-stage deep learning network
Identifying players from videos in each play is also essential for the indexing of player participation.
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.
Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images
To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality.
