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pro vyhledávání: '"Lu, Charles"'
Decentralized data markets can provide more equitable forms of data acquisition for machine learning. However, to realize practical marketplaces, efficient techniques for seller selection need to be developed. We propose and benchmark federated data
Externí odkaz:
http://arxiv.org/abs/2406.04257
Autor:
Lu, Charles, Huang, Baihe, Karimireddy, Sai Praneeth, Vepakomma, Praneeth, Jordan, Michael, Raskar, Ramesh
Acquiring high-quality training data is essential for current machine learning models. Data markets provide a way to increase the supply of data, particularly in data-scarce domains such as healthcare, by incentivizing potential data sellers to join
Externí odkaz:
http://arxiv.org/abs/2403.13893
Autor:
Wang, Lu, Chang, Li, Zhang, Ruipeng, Li, Kexun, Wang, Yu, Chen, Wei, Feng, Xuanlin, Sun, Mingwei, Wang, Qi, Lu, Charles Damien, Zeng, Jun, Jiang, Hua
Background and Objectives: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. Methods and Study Design: We conducted a study of adult septic patients in Intensive Care Unit (ICU), collecting 4
Externí odkaz:
http://arxiv.org/abs/2402.02201
Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal predic
Externí odkaz:
http://arxiv.org/abs/2305.17564
Estimating the test performance of software AI-based medical devices under distribution shifts is crucial for evaluating the safety, efficiency, and usability prior to clinical deployment. Due to the nature of regulated medical device software and th
Externí odkaz:
http://arxiv.org/abs/2207.05796
The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic ways. A lack of statistically rigorous uncertainty quantification is a
Externí odkaz:
http://arxiv.org/abs/2207.02238
Breast cancer is the most common cancers and early detection from mammography screening is crucial in improving patient outcomes. Assessing mammographic breast density is clinically important as the denser breasts have higher risk and are more likely
Externí odkaz:
http://arxiv.org/abs/2206.12008
Autor:
Esserman, Denise, Greene, Erich J., Latham, Nancy K., Kane, Michael, Lu, Charles, Peduzzi, Peter N., Gill, Thomas M., Ganz, David A.
Publikováno v:
In Contemporary Clinical Trials July 2024 142
Publikováno v:
In Clinical Nutrition ESPEN June 2024 61:203-211
Federated learning has attracted considerable interest for collaborative machine learning in healthcare to leverage separate institutional datasets while maintaining patient privacy. However, additional challenges such as poor calibration and lack of
Externí odkaz:
http://arxiv.org/abs/2110.07661