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Xiaoli Yin on Muck Rack

Xiaoli Yin

As seen in: Nature, arXiv, MDPI, Royal Society of Chemistry, Optica
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Articles

Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial

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By Xiaoming Zhang, Xu Han, Xiaoli Yin, Jian Zhou, Kai Cao, Qi Liu, Beilei Cui, Qianqian Zhang, Jianyi Qiao, Ming Sun, Wei Liu, Yuan Z. Gao, Shiling Zhong, Ruobing Bai, Tianan Jiang, Olivier Humbert, Sébastien Ourselin, Ke Yan, Qi Zhang, Yang Hou, Tingbo Liang, Ling Zhang, Yu Shi, Chaoya Zhang, Yingshan Gao, Maria A. Zuluaga
| Nature Verified
Abstract Liver malignancies are frequently evaluated on contrast-enhanced computed tomography (CE-CT), but missed or delayed diagnoses remain a clinically important challenge in high-volume, real-world radiology workflows, highlighting the need for scalable diagnostic safety net approaches.
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Untargeted Metabolomics Reveals the Metabolic Characteristics and Biomarkers of Antioxidant Properties of Gardeniae Fructus from Different Geographical Origins in China

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By Wu Jiang, Lingling Jiang, Xiaoli Yin, Shuhui Zhang
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Composite underwater optical wireless channel modeling based on the electric field Monte Carlo method

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By Long Wang, Xiaoguang Jin, Xiaoli Yin
| Optica Verified
In this study, we propose a simulation method to investigate the scattering characteristics of beams with specific phase structures in waters containing micrometer-scale particles and bubbles. This method is based on the electric field Monte Carlo (EMC) algorithm and utilizes the Mie theory and ray tracing method.
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