5 citations · 9 across the 8 of their papers we have counts for
8 papers
SCPAT-GAN: Structural Constrained and Pathology Aware Convolutional Transformer-GAN for Virtual Histology Staining of Human Coronary OCT images
Xueshen Li, Hongshan Liu, Xiaoyu Song +3
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…
Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network
Xueshen Li, Zhenxing Dong, Hongshan Liu +3
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can…
Structural constrained virtual histology staining for human coronary imaging using deep learning
Xueshen Li, Hongshan Liu, Xiaoyu Song +3
Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this pape…
Roles of scattered and ballistic photons in imaging through scattering media: a deep learning-based study
Xuyu Zhan, Jingjing Gao, Yu Gan +6
Scattering of light in complex media scrambles optical wavefronts and breaks the principles of conventional imaging methods. For decades, researchers have endeavored to conquer the…
Automated player identification and indexing using two-stage deep learning network
Hongshan Liu, Colin Aderon, Noah Wagon +5
American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation.…
Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images
Yaoqi Tang, Yufan Li, Hongshan Liu +5
Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical…
Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution
Zhenxing Dong, Hong Cao, Wang Shen +4
Current learning-based single image super-resolution (SISR) algorithms underperform on real data due to the deviation in the assumed degrada-tion process from that in the real-worl…
Co-Seg: An Image Segmentation Framework Against Label Corruption
Ziyi Huang, Haofeng Zhang, Andrew Laine +3
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training. Neural networks can gradually overfit corrupted labels if directly tra…