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Ruyin Tian

As seen in: Nature
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Articles

Uncertainty-inspired open-set model for identifying infantile fundus abnormalities

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By Xinyu Zhao, Zhenquan Wu, Xuemei Zhu, Mengrui Zhang, Kaixuan Cui, Shaobin Chen, Sihui Zhang, Yarou Hu, Shirou Wu, Ruyin Tian, Yi Chen, Xiaorong Cheng, Na Duan, Ling Jiang, Wenbin Wei, Wei Chi, Guishuang Ying, Baiying Lei, Jianhong Liang, Guoming Zhang
| Nature Verified
Abstract Accurate and safe identification of infantile fundus abnormalities is critical for large-scale screening, yet remains challenging because of heterogeneous disease presentations and overconfident predictions by conventional artificial intelligence (AI) models. In this study, we aim to develop an uncertainty-inspired open-set (UIOS) system to address these challenges.
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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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