Ninon Burgos
123
Documents
Affiliations actuelles
- Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris] (ARAMIS)
- Institut du Cerveau = Paris Brain Institute (ICM)
Identifiants chercheurs
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ninon-burgos
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0000-0002-4668-2006
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25099884X
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burgos_n_1
- ResearcherID : U-3404-2018
Site web
Présentation
Publications
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Publications
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ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing3IA Doctoral Workshop, Nov 2021, Toulouse, France
Poster de conférence
hal-03423072
v2
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Visualization approach to assess the robustness of neural networks for medical image classificationICM days 2019, Jan 2020, Louan, France
Poster de conférence
hal-03365775
v1
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Identification of unlabeled latent subtypes with saliency mapsICM welcome days, Oct 2020, Paris (online), France
Poster de conférence
hal-03365788
v1
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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
Poster de conférence
hal-03365742
v1
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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⟩
Ouvrages
hal-03721959
v1
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Reproducibility in medical image computing: what is it and how is it assessed?Marco Lorenzi and Maria Zuluaga. Trustworthy AI in Medical Imaging, Elsevier, pp.177-204, 2024, MICCAI Book Series, Elsevier, ⟨10.1016/B978-0-44-323761-4.00018-3⟩
Chapitre d'ouvrage
hal-04895884
v1
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Neuroimaging in Machine Learning for Brain DisordersOlivier Colliot. Machine Learning for Brain Disorders, 197, Springer, pp.253-284, 2023, Neuromethods, ⟨10.1007/978-1-0716-3195-9_8⟩
Chapitre d'ouvrage
hal-03814787
v1
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Deep learning: basics and convolutional neural networks (CNN)Olivier Colliot. Machine Learning for Brain Disorders, Springer, 2023, ⟨10.1007/978-1-0716-3195-9_3⟩
Chapitre d'ouvrage
hal-03957224
v2
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Reproducibility in machine learning for medical imagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, 2023
Chapitre d'ouvrage
hal-03957240
v2
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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-03615163
v2
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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⟩
Chapitre d'ouvrage
hal-03721950
v1
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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⟩
Chapitre d'ouvrage
hal-03721947
v1
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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⟩
Chapitre d'ouvrage
hal-03721697
v1
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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⟩
Chapitre d'ouvrage
hal-03721967
v1
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Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practiceMedical Imaging. Sorbonne Université, 2022
HDR
tel-03941953
v1
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