20 Papers
12 Citations
Lei You is an academic researcher from University of Texas Health Science Center at Houston. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 13 publications. Previous affiliations of Lei You include University Town of Shenzhen & Harbin Institute of Technology.
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Papers
Generative Adversarial Networks and Its Applications in Biomedical Informatics.
TL;DR: The origin, specific working principle, and development history of GAN are introduced, various applications of GAn in digital image processing, Cycle-GAN, and its application in medical imaging analysis, as well as the latest applications in medical informatics and bioinformatics are introduced.
scIGANs: single-cell RNA-seq imputation using generative adversarial networks
TL;DR: This work demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.
A Hybrid CNN for Image Denoising
TL;DR: The proposed hybrid denoising CNN (HDCNN) is composed of a dilated block (DB), RepVGG block (RVB), feature refinement block (FB) and a single convolution, which makes the HDCNN have good performance in image Denoising.
A Framework for Big Data Governance to Advance RHINs: A Case Study of China
TL;DR: A big data governance framework with 3 domains and 12 elements was presented based on Chinese practice, which might serve as valuable references for the cross-dimensional development of RHINs, provide overall guidance for the sustainable development of regional health informatization, and contribute to realizing the business value of healthcare big data.
Automated Sagittal Craniosynostosis Classification from CT Images Using Transfer Learning.
Lei You,Guangming Zhang,Weiling Zhao,Matthew Greives R,Matthew Greives R,Lisa R. David,Xiaobo Zhou +6 more
- 27 Feb 2020
TL;DR: A deep learning-based method to learn advanced features for the classification of CSO subtypes can be more stable, approximate the diagnosis performance of physicians and have the potential to reduce the inter-observer variability thereby providing clinical insight into research and the treatment selection in patients with CSO.
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