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MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging

Autor: Liu, Yuanye, Gao, Zheyao, Shi, Nannan, Wu, Fuping, Shi, Yuxin, Chen, Qingchao, Zhuang, Xiahai

Accurate staging of liver fibrosis from magnetic resonance imaging (MRI) is crucial in clinical practice. While conventional methods often focus on a specific sub-region, multi-view learning captures more information by analyzing multiple patches sim

Externí odkaz: http://arxiv.org/abs/2405.02918

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Fairness-Aware Job Scheduling for Multi-Job Federated Learning

Autor: Shi, Yuxin, Yu, Han

Federated learning (FL) enables multiple data owners (a.k.a. FL clients) to collaboratively train machine learning models without disclosing sensitive private data. Existing FL research mostly focuses on the monopoly scenario in which a single FL ser

Externí odkaz: http://arxiv.org/abs/2401.02740

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Fairness-Aware Client Selection for Federated Learning

Autor: Shi, Yuxin, Liu, Zelei, Shi, Zhuan, Yu, Han

Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server can only engage a limited number of clients in each training round, FL c

Externí odkaz: http://arxiv.org/abs/2307.10738

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A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging

Autor: Gao, Zheyao, Liu, Yuanye, Wu, Fuping, Shi, NanNan, Shi, Yuxin, Zhuang, Xiahai

Staging of liver fibrosis is important in the diagnosis and treatment planning of patients suffering from liver diseases. Current deep learning-based methods using abdominal magnetic resonance imaging (MRI) usually take a sub-region of the liver as a

Externí odkaz: http://arxiv.org/abs/2306.12054

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Akademický článek

Enhanced Capacitive Deionization of Hollow Mesoporous Carbon Spheres/MOFs Derived Nanocomposites by Interface‐Coating and Space‐Encapsulating Design.

Autor: Tang, Yijian¹ (AUTHOR), Shi, Yuxin¹ (AUTHOR), Su, Yichun¹ (AUTHOR), Cao, Shuai¹ (AUTHOR), Hu, Jinliang² (AUTHOR), Zhou, Huijie¹ (AUTHOR), Sun, Yangyang¹ (AUTHOR), Liu, Zheng¹ (AUTHOR), Zhang, Songtao¹ (AUTHOR), Xue, Huaiguo¹ (AUTHOR), Pang, Huan¹ (AUTHOR) panghuan@yzu.edu.cn

Publikováno v: Advanced Science. 10/23/2024, Vol. 11 Issue 39, p1-10. 10p.