Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Yao, Michael S."'
Autor:
Yang, Yue, Gandhi, Mona, Wang, Yufei, Wu, Yifan, Yao, Michael S., Callison-Burch, Chris, Gee, James C., Yatskar, Mark
While deep networks have achieved broad success in analyzing natural images, when applied to medical scans, they often fail in unexcepted situations. We investigate this challenge and focus on model sensitivity to domain shifts, such as data sampled
Externí odkaz:
http://arxiv.org/abs/2405.14839
Autor:
Wu, Yifan, Liu, Yang, Yang, Yue, Yao, Michael S., Yang, Wenli, Shi, Xuehui, Yang, Lihong, Li, Dongjun, Liu, Yueming, Gee, James C., Yang, Xuan, Wei, Wenbin, Gu, Shi
Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification. The advent of machine learning offers a promising solution, while the development of such technologies
Externí odkaz:
http://arxiv.org/abs/2403.05606
Autor:
Yao, Michael S., Zeng, Yimeng, Bastani, Hamsa, Gardner, Jacob, Gee, James C., Bastani, Osbert
Offline model-based policy optimization seeks to optimize a learned surrogate objective function without querying the true oracle objective during optimization. However, inaccurate surrogate model predictions are frequently encountered along the opti
Externí odkaz:
http://arxiv.org/abs/2402.06532
Autor:
Yao, Michael S., Chae, Allison, MacLean, Matthew T., Verma, Anurag, Duda, Jeffrey, Gee, James, Torigian, Drew A., Rader, Daniel, Kahn, Charles, Witschey, Walter R., Sagreiya, Hersh
Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As the time available for clinical office visits shortens and medical imaging data become more widely available, pat
Externí odkaz:
http://arxiv.org/abs/2209.10043
Autor:
Yao, Michael S., Hansen, Michael S.
Publikováno v:
In Proceedings of the 2nd Machine Learning for Health Symposium 193:489-511, 2022
Accelerated MRI reconstructs images of clinical anatomies from sparsely sampled signal data to reduce patient scan times. While recent works have leveraged deep learning to accomplish this task, such approaches have often only been explored in simula
Externí odkaz:
http://arxiv.org/abs/2208.12835
Akademický článek
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Autor:
Chae, Allison, Yao, Michael S., Sagreiya, Hersh, Goldberg, Ari D., Chatterjee, Neil, MacLean, Matthew T., Duda, Jeffrey, Elahi, Ameena, Borthakur, Arijitt, Ritchie, Marylyn D., Rader, Daniel, Kahn, Charles E., Witschey, Walter R., Gee, James C.
Publikováno v:
Radiology; January 2024, Vol. 310 Issue: 1
SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification.
Autor:
Yao MS; Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, USA.; Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA., Chae A; Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA., MacLean MT; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA., Verma A; Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA., Duda J; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA., Gee JC; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA., Torigian DA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA., Rader D; Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA., Kahn CE Jr; Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA., Witschey WR; Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA., Sagreiya H; Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Publikováno v:
PRedictive Intelligence in MEdicine. PRIME (Workshop) [Predict Intell Med] 2023 Oct; Vol. 14277, pp. 46-57.