Ninon Burgos
- 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)
-
ninon-burgos
-
0000-0002-4668-2006
-
25099884X
-
burgos_n_1
- ResearcherID : U-3404-2018
Présentation
Publications
Publications
|
|
Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detectionISBI 2026 - International Symposium on Biomedical Imaging, Apr 2026, London, United Kingdom |
|
|
Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG pet in the context of Alzheimer’s diseaseDGM4MICCAI 2025 - Deep Generative Models, Sep 2025, Daejong, South Korea. pp.254-264, ⟨10.1007/978-3-032-05472-2_25⟩ |
|
|
Comparing Longitudinal Preprocessing Pipelines for Brain Volume Consistency in T1-Weighted MRI Test-Retest ScansSPIE Medical Imaging 2026, Feb 2026, Vancouver, Canada |
|
|
Étude de la qualité des IRM T1 et FLAIR de routine clinique au sein d'un entrepôt de données de santé de l'AP-HPIABM 2026 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2026, Lyon, France |
|
|
Assessing the Efficacy of Artefact Synthesis and Transfer Learning for Quality Control in Clinical 3D FLAIR Brain MRISPIE Medical Imaging 2026, Feb 2026, Vancouver, Canada |
|
|
Unsupervised anomaly detection in brain FDG PET with deep generative models: An experimental analysis of model variability and mitigation strategiesSPIE Medical Imaging 2026, Feb 2026, Vancouver, Canada |
|
|
Simulation d'artefacts pour le contrôle automatique de la qualité d'IRM cérébrales FLAIR en routine cliniqueColloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM 2025), Mar 2025, Nice, France |
|
|
Exploitation de la variabilité de la population contrôle pour la détection d'anomalies non supervisée par apprentissage profond en imagerie cérébrale FDG PETIABM 2024 - Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale, Mar 2024, Grenoble, France |
|
|
Clinica, an open-source software to facilitate neuroimaging studiesColloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM), Mar 2024, Grenoble, France |
|
|
Leveraging noise and contrast simulation for the automatic quality control of routine clinical T1-weighted brain MRISPIE Medical Imaging 2024: Image Processing, Feb 2024, San Diego (CA), United States. ⟨10.1117/12.3005781⟩ |
|
|
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PETSPIE Medical Imaging, Feb 2024, San Diego, United States. ⟨10.1117/12.2691683⟩ |
|
|
Evaluation of pseudo-healthy reconstruction for anomaly detection in brain FDG PETMIDL 2024 - Medical Imaging with Deep Learning, Jul 2024, Paris, France |
|
|
Confidence intervals uncovered: Are we ready for real-world medical imaging AI?MICCAI 2024 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, Oct 2024, Marrakech, Morocco. pp.124-132, ⟨10.1007/978-3-031-72117-5_12⟩ |
|
|
The intriguing effect of frequency disentangled learning on medical image segmentationMedical Imaging 2024, Feb 2024, San Diego, CA, United States. pp.49, ⟨10.1117/12.2692286⟩ |
|
|
Generating PET-derived maps of myelin content from clinical MRI using curricular discriminator training in generative adversarial networksSPIE Medical Imaging, Feb 2024, San Diego, United States. ⟨10.1117/12.3004975⟩ |
|
|
Detecting Brain Anomalies in Clinical Routine with the β-VAE: Feasibility Study on Age-Related White Matter HyperintensitiesMedical Imaging with Deep Learning - MIDL 2024, Jul 2024, Paris, France |
|
|
Recent advances in the open-source ClinicaDL software for reproducible neuroimaging with deep learningSPIE Medical Imaging, Feb 2024, San Diego, United States. pp.519-524, ⟨10.1117/12.3006039⟩ |
|
|
From Nipype to Pydra: a Clinica storyOHBM 2023 - Annual meeting of the Organization for Human Brain Mapping, Jul 2023, Montreal, Canada |
|
|
Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based approachesDeep Generative Models workshop at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Oct 2023, Vancouver, Canada |
|
|
Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data WarehouseDART 2023 - 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, Oct 2023, Vancouver (BC), Canada. pp.84-93, ⟨10.1007/978-3-031-45857-6_9⟩ |
|
|
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography TranslationBMVC 2023, 34th British Machine Vision Conference, Nov 2023, Aberdeen, United Kingdom |
|
|
How can data augmentation improve attribution maps for disease subtype explainability?SPIE Medical Imaging, Feb 2023, San Diego, United States |
|
|
Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PETSPIE Medical Imaging, Feb 2023, San Diego, United States |
|
|
Transfer learning from synthetic to routine clinical data for motion artefact detection in brain T1-weighted MRISPIE Medical Imaging 2023: Image Processing, Feb 2023, San Diego, United States |
|
|
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 |
|
|
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 |
|
|
MRI field strength predicts Alzheimer's disease: a case example of bias in the ADNI data setISBI 2022 - International Symposium on Biomedical Imaging, Mar 2022, Kolkata, India. ⟨10.1109/ISBI52829.2022.9761504⟩ |
|
|
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⟩ |
|
|
Clinica: an open-source software platform for reproducible clinical neuroscience studiesMRI Together 2021 - A global workshop on Open Science and Reproducible MR Research, Dec 2021, Online, France |
|
|
New longitudinal and deep learning pipelines in the Clinica software platformOHBM 2020 - Annual meeting of the Organization for Human Brain Mapping, Jun 2020, Montreal / Virtual, Canada |
|
|
Visualization approach to assess the robustness of neural networks for medical image classificationSPIE Medical Imaging 2020, Feb 2020, Houston, United States. ⟨10.1117/12.2548952⟩ |
|
|
How serious is data leakage in deep learning studies on Alzheimer’s disease classification?Organization for Human Brain Mapping (OHBM), Jun 2019, Roma, Italy |
|
|
Deciphering the progression of PET alterations using surface-based spatiotemporal modelingOHBM 2019 - Annual meeting of the Organization for Human Brain Mapping, Jun 2019, Rome, Italy |
|
|
Beware of feature selection bias! Example on Alzheimer's disease classification from diffusion MRI2019 OHBM Annual Meeting - Organization for Human Brain Mapping, Jun 2019, Rome, Italy |
|
|
How serious is data leakage in deep learning studies on Alzheimer's disease classification?2019 OHBM Annual meeting - Organization for Human Brain Mapping, Jun 2019, Rome, Italy |
|
|
New advances in the Clinica software platform for clinical neuroimaging studiesOHBM 2019 - Annual Meeting on Organization for Human Brain Mapping, Jun 2019, Roma, Italy. ⟨10.1016/j.neuroimage.2011.09.015⟩ |
|
|
Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible studyOHBM 2019 - Organization for Human Brain Mapping Annual Meeting 2019, Jun 2019, Rome, Italy |
|
|
Prediction of future cognitive scores and dementia onset in Mild Cognitive Impairment patientsOHBM 2019 - Organization for Human Brain Mapping Conference, Jun 2019, Rome, Italy |
|
|
Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging dataSPIE Medical Imaging 2019, Feb 2019, San Diego, United States. ⟨10.1117/12.2512430⟩ |
|
|
Three simple ideas for predicting progression to Alzheimer's disease8th International Workshop on Pattern Recognition in Neuroimaging, Jun 2018, Singapour, Singapore |
|
|
Clinica: an open source software platform for reproducible clinical neuroscience studiesAnnual meeting of the Organization for Human Brain Mapping - OHBM 2018, Jun 2018, Singapore, Singapore |
|
|
Reproducible evaluation of Alzheimer's Disease classification from MRI and PET dataAnnual meeting of the Organization for Human Brain Mapping - OHBM 2018, Jun 2018, Singapour, Singapore |
|
|
Using diffusion MRI for classification and prediction of Alzheimer's Disease: a reproducible studyAAIC 2018 - Alzheimer's Association International Conference, Jul 2018, Chicago, United States |
|
|
Comparison of DTI Features for the Classification of Alzheimer's Disease: A Reproducible StudyOHBM 2018 - Organization for Human Brain Mapping Annual Meeting, Jun 2018, Singapour, Singapore |
|
|
A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNIAnnual meeting of the Organization for Human Brain Mapping - OHBM 2018, Jun 2018, Singapour, Singapore |
|
|
A comparison of techniques for quantifying amyloid burden on a combined PET/MR scannerAAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P12--P13, ⟨10.1016/j.jalz.2017.06.2276⟩ |
|
|
Midlife affective symptoms are associated with lower brain volumes in later life: Evidence from a prospective UK birth cohortAAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P212, ⟨10.1016/j.jalz.2017.07.086⟩ |
|
|
Individual Analysis of Molecular Brain Imaging Data Through Automatic Identification of Abnormality PatternsComputational Methods for Molecular Imaging - [MICCAI 2017 Satellite Workshop], Sep 2017, Quebec City, Canada |
|
|
Short acquisition time PET quantification using MRI-based pharmacokinetic parameter synthesisMedical Image Computing and Computer-Assisted Intervention − MICCAI 2017, 2017, Québec, Canada. pp.737--744, ⟨10.1007/978-3-319-66185-8_83⟩ |
Brain volume, cerebral β-amyloid deposition, and ageing: A study of over 200 individuals born in the same week in 1946AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P1464--P1465, ⟨10.1016/j.jalz.2017.07.534⟩ |
|
Geometric and Dosimetric Evaluation of Three Atlas-based Segmentation Methods for Head and Neck Cancer Patients on MR ImagesMR in RT symposium, Jun 2017, Sydney, Australia |
|
|
|
Exploring the population prevalence of β-amyloid burden: An analysis of 250 individuals born in mainland Britain in the same week in 1946AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P1088--P1089, ⟨10.1016/j.jalz.2017.06.1563⟩ |
|
|
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's DiseaseMachine Learning in Medical Imaging 2017, Sep 2017, Quebec City, Canada. pp.8 |
|
|
Early Diagnosis of Alzheimer’s Disease Using Subject-Specific Models of FDG-PET DataAAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.1-2, ⟨10.1016/j.jalz.2017.06.1618⟩ |
|
|
Diagnosis of Alzheimer’s Disease Through Identification of Abnormality Patterns in FDG PET Data30th Annual Congress of the European Association of Nuclear Medicine (EANM), Oct 2017, Vienna, Austria. pp.253 - 254, ⟨10.1007/s00259-017-3822-1⟩ |
Multi atlas-based attenuation correction for brain FDG- PET imaging using a TOF-PET/MR scanner: Comparison with clinical single atlas- and CT-based attenuation correctionScientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine – ISMRM 2016, May 2016, Singapore, Singapore |
|
Simultaneous organ-at-risk segmentation and CT synthesis in the pelvic region for MRI-only radiotherapy treatment planningInternational Conference on the use of Computers in Radiation Therapy – ICCR 2016, Jun 2016, London, United Kingdom |
|
A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patientsIEEE Nuclear Science Symposium and Medical Imaging Conference – IEEE NSS/MIC 2016, Oct 2016, Strasbourg, France |
|
|
|
Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planningMedical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Oct 2016, Athens, Greece. pp.547--555, ⟨10.1007/978-3-319-46723-8_63⟩ |
NiftyWeb: web based platform for image processing on the cloudScientific Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine – ISMRM 2016, May 2016, Singapore, Singapore |
|
A multi-method, multi-center study of PET/MRI brain attenuation correction on a large cohort of [18F]- FDG patients: ready for clinical implementationRSNA 216 – Annual Meeting of the Radiological Society of North America, Nov 2016, Chicago, United States |
|
CT synthesis in the head & neck and pelvic regions for radiotherapy treatment planningIPEM Workshop on MRI Guided Radiotherapy, Mar 2016, Sheffield, United Kingdom |
|
|
|
Partial Volume Correction of Amyvid and FDG PET data using the discrete iterative Yang techniqueAnnual Congress of the European Association of Nuclear Medicine – EANM 2015, Oct 2015, Hamburg, Germany. pp.S69, ⟨10.1007/s00259-015-3198-z⟩ |
|
|
CT synthesis in the head & neck region for PET/MR attenuation correction: an iterative multi-atlas approachConference on PET/MR and SPECT/MR – PSMR 2015, May 2015, Elba, Italy. pp.A31, ⟨10.1186/2197-7364-2-S1-A31⟩ |
|
|
Multi-atlas synthesis for computer assisted diagnosis: Application to cardiovascular diseasesIEEE International Symposium on Biomedical Imaging – IEEE ISBI 2015, Apr 2015, New-York, United States. pp.290--293, ⟨10.1109/ISBI.2015.7163870⟩ |
|
|
Detail-preserving PET reconstruction with sparse image representation and anatomical priorsInformation Processing in Medical Imaging – IPMI 2015, Jun 2015, Isle of Skye, United Kingdom. pp.540--551, ⟨10.1007/978-3-319-19992-4_42⟩ |
|
|
Robust CT synthesis for radiotherapy planning: Application to the head & neck regionMedical Image Computing and Computer-Assisted Intervention − MICCAI 2015, Oct 2015, Munich, Germany. pp.476--484, ⟨10.1007/978-3-319-24571-3_57⟩ |
Evaluation of different approaches to obtain synthetic CT images for a MRI-only radiotherapy workflowMR in RT symposium, Jun 2015, Lund, Sweden |
|
|
|
Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MRConference on PET/MR and SPECT/MR – PSMR 2015, May 2015, Elba, Italy. pp.A44, ⟨10.1186/2197-7364-2-S1-A44⟩ |
|
|
Subject-specific models for the analysis of pathological FDG PET dataMedical Image Computing and Computer-Assisted Intervention − MICCAI 2015, Oct 2015, Munich, Germany. pp.651--658, ⟨10.1007/978-3-319-24571-3_78⟩ |
|
|
Effect of scatter correction when comparing attenuation maps: Application to brain PET/MRIEEE Nuclear Science Symposium and Medical Imaging Conference – IEEE NSS/MIC 2014, Nov 2014, Seattle, United States. pp.1--5, ⟨10.1109/NSSMIC.2014.7430775⟩ |
|
|
Attenuation correction synthesis for hybrid PET-MR scanners: validation for brain study applicationsConference on PET/MR and SPECT/MR – PSMR 2014, May 2014, Kos, Greece. pp.A52, ⟨10.1186/2197-7364-1-S1-A52⟩ |
|
|
Image reconstruction of mMR PET data using the open source software STIRConference on PET/MR and SPECT/MR – PSMR 2014, May 2014, Kos, Greece. pp.A44, ⟨10.1186/2197-7364-1-S1-A44⟩ |
|
|
Simulated field maps: Toward improved susceptibility artefact correction in interventional MRIInformation Processing in Computer-Assisted Interventions – IPCAI 2014, Jun 2014, Fukuoka, Japan. pp.226--235, ⟨10.1007/978-3-319-07521-1_24⟩ |
Joint parametric reconstruction and motion correction framework for dynamic PET dataMedical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Sep 2014, Boston, United States. pp.114-121, ⟨10.1007/978-3-319-10404-1_15⟩ |
|
Attenuation correction synthesis for hybrid PET-MR scannersMedical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Sep 2013, Nagoya, Japan. pp.147--154, ⟨10.1007/978-3-642-40811-3_19⟩ |
Biomedical Image Synthesis and SimulationNinon Burgos; David Svoboda. Elsevier, 2022, 978-0-12-824349-7. ⟨10.1016/C2020-0-01250-8⟩ |
|
|
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⟩ |
|
|
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⟩ |
|
|
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⟩ |
|
|
Reproducibility in machine learning for medical imagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, 2023 |
|
|
Interpretability of Machine Learning Methods Applied to NeuroimagingOlivier Colliot. Machine Learning for Brain Disorders, Springer, 2023, ⟨10.1007/978-1-0716-3195-9_22⟩ |
|
|
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⟩ |
|
|
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⟩ |
|
|
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⟩ |
|
|
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⟩ |
|
|
Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practiceMedical Imaging. Sorbonne Université, 2022 |