Zobrazeno 1 - 8 of 8 pro vyhledávání: '"Kola Babalola"'
1
Determining Mice Sex from Chest X-rays using Deep Learning
Autor: Kola Babalola, Abiodun Ajiboye
Publikováno v: 2020 IEEE 2nd International Conference on Cyberspac (CYBER NIGERIA).
This Following on from work by Babalola et al. It is shown that the sex of mice can be determined from x-ray images of the chest region alone using convolutional neural networks. The anatomical differences that may be responsible for this is further
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::251923fe9494796ecb3f3bedd340ea51
https://doi.org/10.1109/cybernigeria51635.2021.9428822
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2
A dynamic COVID-19 immune signature includes associations with poor prognosis
Autor: Aislinn Jennings, Isaac Francos-Quijorna, Manu Shankar-Hari, Adrian Hayday, Duncan R. McKenzie, John Cason, Thomas Hayday, Shraddha Kamdar, Yin Wu, Richard Davis, Matthew Fish, Kate Brooks, Blair Merrick, Katie J. Doores, Eva Bugallo-Blanco, Adam Laing, Kai Kisand, Jeremy Mason, Iva Zlatareva, Kola Babalola, Magdalene Joseph, Francesca Di Rosa, Jeffrey Seow, Philippe Moingeon, Anna Lorenc, Pärt Peterson, Daniel Davies, Mohammad A. A. Ibrahim, Efstathios Theodoridis, Federico Lopez Gomez, Liis Haljasmägi, Pierre Vantourout, Vasiliki Sofra, Abhishek Das, Maria Greco, Christine Mant, Sarah Gee, Julie Nuo En Chan, Carl Graham, Leticia Monin, Loubna Chadli, Irene del Molino del Barrio, Miguel Muñoz-Ruiz, Florencia Cano, Karen Bisnauthsing, Lauren Martinez, Sultan Abdul-Jawad, Sarah Ryan, Jonathan D. Edgeworth, Joshua D. Freedman
Publikováno v: Nature medicine
26 (2020): 1623–1635. doi:10.1038/s41591-020-1038-6
info:cnr-pdr/source/autori:Laing, Adam G.; Lorenc, Anna; del Molino del Barrio, Irene; Das, Abhishek; Fish, Matthew; Monin, Leticia; Muñoz-Ruiz, Miguel; McKenzie, Duncan R.; Hayday, Thomas S.; Francos-Quijorna, Isaac; Kamdar, Shraddha; Joseph, Magdalene; Davies, Daniel; Davis, Richard; Jennings, Aislinn; Zlatareva, Iva; Vantourout, Pierre; Wu, Yin; Sofra, Vasiliki; Cano, Florencia; Greco, Maria; Theodoridis, Efstathios; Freedman, Joshua; Gee, Sarah; Chan, Julie Nuo En; Ryan, Sarah; Bugallo-Blanco, Eva; Peterson, Pärt; Kisand, Kai; Haljasmägi, Liis; Chadli, Loubna; Moingeon, Philippe; Martinez, Lauren; Merrick, Blair; Bisnauthsing, Karen; Brooks, Kate; Ibrahim, Mohammad A.A.; Mason, Jeremy; Lopez Gomez, Federico; Babalola, Kola; Abdul-Jawad, Sultan; Cason, John; Mant, Christine; Seow, Jeffrey; Graham, Carl; Doores, Katie J.; Di Rosa, Francesca; Edgeworth, Jonathan; Shankar-Hari, Manu; Hayday, Adrian C./titolo:A dynamic COVID-19 immune signature includes associations with poor prognosis/doi:10.1038%2Fs41591-020-1038-6/rivista:Nature medicine (Print)/anno:2020/pagina_da:1623/pagina_a:1635/intervallo_pagine:1623–1635/volume:26
Nature Medicine
Improved understanding and management of COVID-19, a potentially life-threatening disease, could greatly reduce the threat posed by its etiologic agent, SARS-CoV-2. Toward this end, we have identified a core peripheral blood immune signature across 6
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7f5fa781e4a5acfc0a8216290f81914
http://www.cnr.it/prodotto/i/439414
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3
Investigating Sex Related Phenotype Changes in Knockout Mice by Applying Deep Learning to X-Ray Images
Autor: Jeremy Mason, Kola Babalola, Violeta Muñoz-Fuentes, Terry Meehan, Hamed Haseli Mashhadi
Publikováno v: Communications in Computer and Information Science ISBN: 9783030527907
MIUA
We train a convolutional neural network (CNN) to classify the sex of mice from x-ray images in order to develop a tool that can be used not only for quality control of high throughput image data, but also to identify sex-related phenotype alterations
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d32c999ca999c8976d9c74335646df93
https://doi.org/10.1007/978-3-030-52791-4_28
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5
A Three-Dimensional Numerical Simulator for Expansion-Drive Reservoirs
Autor: Kola Babalola, Ismaila N. Dele, Alpheus O. Igbokoyi
Publikováno v: All Days.
Regime-specific numerical solutions are valuable in studying the behaviour of reservoir processes both for practical field management and for benchmarking the success of analytical solutions within the regime of interest. In this work, a three-dimens
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d608e818348ec7f2aa9cd20592ce34d7
https://doi.org/10.2118/172368-ms
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6
Using parts and geometry models to initialise Active Appearance Models for automated segmentation of 3D medical images
Autor: Timothy F. Cootes, Kola Babalola
Publikováno v: ISBI
In recent years, statistical shape models, of which Active Appearance Models (AAMs) are a subset have been increasingly applied to the automatic segmentation of medical images. AAMs are a local search technique requiring good initialisation. In 3D au
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dfd35918d8e60eb69fd7feaf2e79b055
https://doi.org/10.1109/isbi.2010.5490177
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7
AAM Segmentation of the Mandible and Brainstem
Autor: Kola Babalola, Tim Cootes
Publikováno v: The MIDAS Journal.
The 2009 MICCAI Head and Neck Auto-Segmentation Challenge was aimed at assessing algorithms for automated segmentation of the brain stem and mandible from CT images for use in radiotherapy planning. The organisers provided 10 datasets with associated
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::36e8f20a8c8cf662e7fd4d3567e2a32e
https://doi.org/10.54294/gh21k3
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8
Canonical Correlation Analysis of Sub-cortical Brain Structures Using Non-rigid Registration
Autor: Daniel Rueckert, Kola Babalola, Anil Rao
Publikováno v: Biomedical Image Registration ISBN: 9783540356486
WBIR
In this paper, we present the application of canonical correlation analysis to investigate how the shapes of different structures within the brain vary statistically relative to each other. Canonical correlation analysis is a multivariate statistical
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3be3ae29edd1463ba8e6744e4b17e29a
https://doi.org/10.1007/11784012_9
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