Zobrazeno 1 - 10
of 5 767
pro vyhledávání: '"Pierre, R."'
Autor:
Kumar, Amar, Fathi, Nima, Mehta, Raghav, Nichyporuk, Brennan, Falet, Jean-Pierre R., Tsaftaris, Sotirios, Arbel, Tal
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i.e. confounders), should they be prevalent in the training dataset, rather than on the causal image m
Externí odkaz:
http://arxiv.org/abs/2308.10984
Autor:
Lionel Schiavolin, Dalila Lakhloufi, Gwenaelle Botquin, Geoffrey Deneubourg, Corentin Bruyns, Jenny Steinmetz, Charlotte Henrot, Valérie Delforge, Pierre R. Smeesters, Anne Botteaux
Publikováno v:
Microbiology Spectrum, Vol 12, Iss 10 (2024)
ABSTRACT Streptococcus pyogenes or Group A Streptococcus (GAS) remains a significant infectious problem around the world, particularly in low- and middle-income settings. Moreover, a recent invasive GAS infection (iGAS) upsurge has been observed in h
Externí odkaz:
https://doaj.org/article/64c988b14d2343ec87efdceec5efab9a
Autor:
Dan C Wilkinson, Elizabeth Tallman, Mishal Ashraf, Tatiana Gelaf Romer, Jeehoon Lee, Benjamin Burnett, Pierre R Bushel
Publikováno v:
Bioinformatics and Biology Insights, Vol 18 (2024)
Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences
Externí odkaz:
https://doaj.org/article/970715216ad04d6f9b4dd42e19ea2bab
Autor:
Hu, Anjun, Falet, Jean-Pierre R., Nichyporuk, Brennan S., Shui, Changjian, Arnold, Douglas L., Tsaftaris, Sotirios A., Arbel, Tal
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors
Externí odkaz:
http://arxiv.org/abs/2211.07820
Autor:
Nichyporuk, Brennan, Cardinell, Jillian, Szeto, Justin, Mehta, Raghav, Falet, Jean-Pierre R., Arnold, Douglas L., Tsaftaris, Sotirios A., Arbel, Tal
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 1 (2022)
Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real world consequences. While the failure of models to generalize across
Externí odkaz:
http://arxiv.org/abs/2210.17398