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Baiying Lei

As seen in: Nature, arXiv, Frontiers
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Articles

A fundus image dataset for intelligent diabetic retinopathy system

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By Shuo Yang, Xinyu Zhao, Yongtao Zhang, Qingjie Bai, Yaling Liu, Yi Chen, Kaixuan Cui, Zhen Li Yu, Zhenquan Wu, Baiying Lei, Guoming Zhang, Shaojuan Peng, Duo Yuan, Yarou Hu, Ruyin Tian
| Nature Verified
Abstract Diabetic retinopathy (DR), the most prevalent microvascular complication of diabetes mellitus, is the leading cause of irreversible vision loss in the global working-age population. At present, deep learning-integrated ultra-wide-field (UWF) image analysis systems have improved DR grading consistency and reduced peripheral lesion misdiagnosis rates, thereby overcoming the limitations of traditional 45° viewing field AI models in detecting peripheral retinal lesions.
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[2305.14404] Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI

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By Baiying Lei, Ning Zhong, Yi Pan, Shuqiang Wang
| arXiv
arXiv:2305.14404 (q-bio) Download a PDF of the paper titled Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI, by Qiankun Zuo and 4 other authors Download PDF Submission history From: Shuqiang Wang [ view email] [v1] Tue, 23 May 2023 11:19:02 UTC (15,925 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart...
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A Hierarchical Graph Learning Model for Brain Network Regression Analysis

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By Xi Jiang, Li Wang, Baiying Lei
| Frontiers
1. Introduction Understanding brain structural and functional changes and its relationship to other phenotypes (e.g., behavior and demographical variables or clinical outcomes) are of prime importance in the neuroscience field. One of the key research directions is to use neuroimaging data for predictive or regression analyses and identify phenotype-related imaging biomarkers.
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