Ninon Burgos
101
Documents
Researcher identifiers
ninon-burgos
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0000-0002-4668-2006
- Google Scholar : https://scholar.google.co.uk/citations?user=lHuYSU0AAAAJ&hl=en
- IdRef : 25099884X
- ResearcherId : U-3404-2018
Presentation
[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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ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing3IA Doctoral Workshop, Nov 2021, Toulouse, France
Conference poster
hal-03423072v2
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Visualization approach to assess the robustness of neural networks for medical image classificationICM days 2019, Jan 2020, Louan, France
Conference poster
hal-03365775v1
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Identification of unlabeled latent subtypes with saliency mapsICM welcome days, Oct 2020, Paris (online), France
Conference poster
hal-03365788v1
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How serious is data leakage in deep learning studies on Alzheimer’s disease classification?Organization for Human Brain Mapping (OHBM), Jun 2019, Roma, Italy
Conference poster
hal-03365742v1
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Biomedical Image Synthesis and SimulationNinon Burgos; David Svoboda. Elsevier, 2022, 978-0-12-824349-7. ⟨10.1016/C2020-0-01250-8⟩
Books
hal-03721959v1
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Interpretability of Machine Learning Methods Applied to NeuroimagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, inPress
Book sections
hal-03615163v1
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Reproducibility in machine learning for medical imagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, inPress
Book sections
hal-03957240v2
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Medical image synthesis using segmentation and registrationBiomedical Image Synthesis and Simulation, Elsevier, pp.55-77, 2022, 9780128243497. ⟨10.1016/B978-0-12-824349-7.00011-6⟩
Book sections
hal-03721697v1
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Future trends in medical and biomedical image synthesisNinon Burgos; David Svoboda. Biomedical Image Synthesis and Simulation, Elsevier, pp.643-645, 2022, 978-0-12-824349-7. ⟨10.1016/B978-0-12-824349-7.00034-7⟩
Book sections
hal-03721950v1
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Neuroimaging in Machine Learning for Brain DisordersOlivier Colliot. Machine Learning for Brain Disorders, Springer, In press
Book sections
hal-03814787v1
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Introduction to medical and biomedical image synthesisNinon Burgos; David Svoboda. Biomedical Image Synthesis and Simulation, Elsevier, pp.1-3, 2022, 978-0-12-824349-7. ⟨10.1016/B978-0-12-824349-7.00008-6⟩
Book sections
hal-03721967v1
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Validation and evaluation metrics for medical and biomedical image synthesisBiomedical Image Synthesis and Simulation, Elsevier, pp.573-600, 2022, 978-0-12-824349-7. ⟨10.1016/B978-0-12-824349-7.00032-3⟩
Book sections
hal-03721947v1
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Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data2022
Preprints, Working Papers, ...
hal-03910451v1
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Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation2021
Preprints, Working Papers, ...
hal-03497645v1
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Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practiceMedical Imaging. Sorbonne Université, 2022
Habilitation à diriger des recherches
tel-03941953v1
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