Zobrazeno 1 - 10
of 913
pro vyhledávání: '"Rubin, Daniel L."'
Autor:
Tang, Siyi, Dunnmon, Jared A., Qu, Liangqiong, Saab, Khaled K., Baykaner, Tina, Lee-Messer, Christopher, Rubin, Daniel L.
Multivariate biosignals are prevalent in many medical domains, such as electroencephalography, polysomnography, and electrocardiography. Modeling spatiotemporal dependencies in multivariate biosignals is challenging due to (1) long-range temporal dep
Externí odkaz:
http://arxiv.org/abs/2211.11176
Publikováno v:
IEEE Transactions on Artificial Intelligence, 2023
Continuous monitoring of trained ML models to determine when their predictions should and should not be trusted is essential for their safe deployment. Such a framework ought to be high-performing, explainable, post-hoc and actionable. We propose TRU
Externí odkaz:
http://arxiv.org/abs/2207.11290
Objective. Mammography reports document the diagnosis of patients' conditions. However, many reports contain non-standard terms (non-BI-RADS descriptors) and incomplete statements, which can lead to conclusions that are not well-supported by the repo
Externí odkaz:
http://arxiv.org/abs/2202.13618
Publikováno v:
In Pattern Recognition September 2024 153
Autor:
Pati, Sarthak, Kumar, Sourav, Varma, Amokh, Edwards, Brandon, Lu, Charles, Qu, Liangqiong, Wang, Justin J., Lakshminarayanan, Anantharaman, Wang, Shih-han, Sheller, Micah J., Chang, Ken, Singh, Praveer, Rubin, Daniel L., Kalpathy-Cramer, Jayashree, Bakas, Spyridon
Publikováno v:
In Patterns 12 July 2024 5(7)
Autor:
Zambrano Chaves, Juan M., Lenchik, Leon, Gallegos, Isabel O., Blankemeier, Louis, Liang, Tie, Rubin, Daniel L., Willis, Marc H., Chaudhari, Akshay S., Boutin, Robert D.
Publikováno v:
In eBioMedicine May 2024 103
Federated learning enables multiple institutions to collaboratively train machine learning models on their local data in a privacy-preserving way. However, its distributed nature often leads to significant heterogeneity in data distributions across i
Externí odkaz:
http://arxiv.org/abs/2107.08371
Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different institutions are usually heterogeneous across institutions, which may reduce
Externí odkaz:
http://arxiv.org/abs/2107.02375
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models. Most work to date assessing disease severity o
Externí odkaz:
http://arxiv.org/abs/2105.08147
Autor:
Tang, Siyi, Dunnmon, Jared A., Saab, Khaled, Zhang, Xuan, Huang, Qianying, Dubost, Florian, Rubin, Daniel L., Lee-Messer, Christopher
Publikováno v:
ICLR 2022
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain unaddressed in prior automated seizure detection and classification stud
Externí odkaz:
http://arxiv.org/abs/2104.08336