Ninon Burgos
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Documents
Identifiants chercheurs
- ninon-burgos
- 0000-0002-4668-2006
- Google Scholar : https://scholar.google.co.uk/citations?user=lHuYSU0AAAAJ&hl=en
- IdRef : 25099884X
- ResearcherId : U-3404-2018
Présentation
[Ninon Burgos](https://ninonburgos.com/) is a CNRS researcher at the [Paris Brain Institute](http://icm-institute.org/) in the [ARAMIS Lab](http://www.aramislab.fr/). She completed her PhD at University College London in the [Centre for Medical Image Computing](http://www.ucl.ac.uk/medical-image-computing). She received an MSc in Biomedical Engineering from Imperial College London and an Engineering degree from a French Graduate School in Electrical Engineering and Computer Science (ENSEA). Her research currently focuses on the development of computational imaging tools to improve the understanding and diagnosis of neurological diseases.
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Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRIBMC Medical Imaging, 2024, 24 (1), pp.67. ⟨10.1186/s12880-024-01242-3⟩
Article dans une revue
hal-03497645v2
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ClinicaDL: an open-source deep learning software for reproducible neuroimaging processingComputer Methods and Programs in Biomedicine, 2022, 220, pp.106818. ⟨10.1016/j.cmpb.2022.106818⟩
Article dans une revue
hal-03351976v2
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Ensemble Learning of Convolutional Neural Network, Support Vector Machine, and Best Linear Unbiased Predictor for Brain Age Prediction: ARAMIS Contribution to the Predictive Analytics Competition 2019 ChallengeFrontiers in Psychiatry, 2020, 11, ⟨10.3389/fpsyt.2020.593336⟩
Article dans une revue
hal-03136463v1
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Recent advances in the open-source ClinicaDL software for reproducible neuroimaging with deep learningSPIE Medical Imaging, Feb 2024, San Diego, United States
Communication dans un congrès
hal-04419141v1
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ClinicaDL: an open-source deep learning software for reproducible neuroimaging processingOHBM 2022 - Annual meeting of the Organization for Human Brain Mapping, Jun 2022, Glasgow, United Kingdom
Communication dans un congrès
hal-04279014v1
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Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation with U-Net derived modelsSPIE Medical Imaging 2022: Image Processing, Feb 2022, San Diego, United States. pp.576-582, ⟨10.1117/12.2608565⟩
Communication dans un congrès
hal-03478798v1
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Advances in the Clinica software platform for clinical neuroimaging studiesOHBM 2022 - Annual meeting of the Organization for Human Brain Mapping, Jun 2022, Glasgow, United Kingdom
Communication dans un congrès
hal-03728243v1
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Interpretability of Machine Learning Methods Applied to NeuroimagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, 2023, ⟨10.1007/978-1-0716-3195-9_22⟩
Chapitre d'ouvrage
hal-03615163v2
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