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#MIGS: Minimally Invasive Gynecologic Surgery Tag Ontology Project.

Journal of minimally invasive gynecology

Davenport A, Hoang E, Hazen N, Brunn E, Kho R, Truong MD, Iglesia CB.
PMID: 34029744
J Minim Invasive Gynecol. 2021 Dec;28(12):2025-2027. doi: 10.1016/j.jmig.2021.05.012. Epub 2021 May 21.

STUDY OBJECTIVE: The creation of an ontology may enable providers to more definitively engage the public in evidence-based and meaningful discussions about women's health. The goal of this study is to review and analyze the current social media status...

Cite
Davenport A, Hoang E, Hazen N, et al. #MIGS: Minimally Invasive Gynecologic Surgery Tag Ontology Project. J Minim Invasive Gynecol. 2021;28(12):2025-2027doi: 10.1016/j.jmig.2021.05.012.
Davenport, A., Hoang, E., Hazen, N., Brunn, E., Kho, R., Truong, M. D., & Iglesia, C. B. (2021). #MIGS: Minimally Invasive Gynecologic Surgery Tag Ontology Project. Journal of minimally invasive gynecology, 28(12), 2025-2027. https://doi.org/10.1016/j.jmig.2021.05.012
Davenport, Abigail, et al. "#MIGS: Minimally Invasive Gynecologic Surgery Tag Ontology Project." Journal of minimally invasive gynecology vol. 28,12 (2021): 2025-2027. doi: https://doi.org/10.1016/j.jmig.2021.05.012
Davenport A, Hoang E, Hazen N, Brunn E, Kho R, Truong MD, Iglesia CB. #MIGS: Minimally Invasive Gynecologic Surgery Tag Ontology Project. J Minim Invasive Gynecol. 2021 Dec;28(12):2025-2027. doi: 10.1016/j.jmig.2021.05.012. Epub 2021 May 21. PMID: 34029744.
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Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

IEEE transactions on neural networks and learning systems

Jeon E, Ko W, Yoon JS, Suk HI.
PMID: 34357871
IEEE Trans Neural Netw Learn Syst. 2021 Aug 06; doi: 10.1109/TNNLS.2021.3100583. Epub 2021 Aug 06.

In recent years, deep learning-based feature representation methods have shown a promising impact on electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by...

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Jeon E, Ko W, Yoon JS, et al. Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI. IEEE Trans Neural Netw Learn Syst. 2021;doi: 10.1109/TNNLS.2021.3100583.
Jeon, E., Ko, W., Yoon, J. S., & Suk, H. I. (2021). Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI. IEEE transactions on neural networks and learning systems, . https://doi.org/10.1109/TNNLS.2021.3100583
Jeon, Eunjin, et al. "Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI." IEEE transactions on neural networks and learning systems vol. (2021). doi: https://doi.org/10.1109/TNNLS.2021.3100583
Jeon E, Ko W, Yoon JS, Suk HI. Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI. IEEE Trans Neural Netw Learn Syst. 2021 Aug 06; doi: 10.1109/TNNLS.2021.3100583. Epub 2021 Aug 06. PMID: 34357871.
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