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Akademický článek

Efficient automated error detection in medical data using deep-learning and label-clustering

Autor: T. V. Nguyen, S. M. Diakiw, M. D. VerMilyea, A. W. Dinsmore, M. Perugini, D. Perugini, J. M. M. Hall

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023)

Abstract Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual inform

Externí odkaz: https://doaj.org/article/2226d610176a46398c5a9f23564ec38c

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Akademický článek

A novel decentralized federated learning approach to train on globally distributed, poor quality, and protected private medical data

Autor: T. V. Nguyen, M. A. Dakka, S. M. Diakiw, M. D. VerMilyea, M. Perugini, J. M. M. Hall, D. Perugini

Publikováno v: Scientific Reports, Vol 12, Iss 1, Pp 1-12 (2022)

Abstract Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralize

Externí odkaz: https://doaj.org/article/1199058a52154e45a2d3007a4508e14b

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Akademický článek

Author Correction: Efficient automated error detection in medical data using deep-learning and label-clustering

Autor: T. V. Nguyen, S. M. Diakiw, M. D. VerMilyea, A. W. Dinsmore, M. Perugini, D. Perugini, J. M. M. Hall

Publikováno v: Scientific Reports, Vol 13, Iss 1, Pp 1-1 (2023)

Externí odkaz: https://doaj.org/article/57081c79a57b45139419f4f8ab66eb0f

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Akademický článek

Automated detection of poor-quality data: case studies in healthcare

Autor: M. A. Dakka, T. V. Nguyen, J. M. M. Hall, S. M. Diakiw, M. VerMilyea, R. Linke, M. Perugini, D. Perugini

Publikováno v: Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)

Abstract The detection and removal of poor-quality data in a training set is crucial to achieve high-performing AI models. In healthcare, data can be inherently poor-quality due to uncertainty or subjectivity, but as is often the case, the requiremen

Externí odkaz: https://doaj.org/article/92ef4fc958d544dcababaa3db903e24e

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A Novel Decentralized Federated Learning Approach to Train on Globally Distributed, Poor Quality, and Protected Private Medical Data

Autor: T V, Nguyen, M A, Dakka, S M, Diakiw, M D, VerMilyea, M, Perugini, J M M, Hall, D, Perugini

Training on multiple diverse data sources is critical to ensure unbiased and generalizable AI. In healthcare, data privacy laws prohibit data from being moved outside the country of origin, preventing global medical datasets being centralized for AI

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9dcebe052a83ceafdd0195e8789392b9
https://doi.org/10.21203/rs.3.rs-1371143/v1

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Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF

Autor: S M Diakiw, J M M Hall, M D VerMilyea, J Amin, J Aizpurua, L Giardini, Y G Briones, A Y X Lim, M A Dakka, T V Nguyen, D Perugini, M Perugini

Publikováno v: Human reproduction (Oxford, England). 37(8)

STUDY QUESTION Can an artificial intelligence (AI) model predict human embryo ploidy status using static images captured by optical light microscopy? SUMMARY ANSWER Results demonstrated predictive accuracy for embryo euploidy and showed a significant

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bbe6edd30ce9cccb69babf99c92427cb
https://pubmed.ncbi.nlm.nih.gov/35674312

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