
Ninon Burgos
120
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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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
Communication dans un congrès
hal-05088701
v1
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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. pp.519-524, ⟨10.1117/12.3006039⟩
Communication dans un congrès
hal-04419141
v1
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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
Communication dans un congrès
hal-04674025
v1
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Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PETSPIE Medical Imaging, Feb 2024, San Diego (California), United States. ⟨10.1117/12.2691683⟩
Communication dans un congrès
hal-04291561
v2
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Clinica, an open-source software to facilitate neuroimaging studiesColloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM), Mar 2024, Grenoble, France
Communication dans un congrès
hal-04653352
v1
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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⟩
Communication dans un congrès
hal-04674029
v1
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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⟩
Communication dans un congrès
hal-04715638
v1
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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⟩
Communication dans un congrès
hal-04654627
v1
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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⟩
Communication dans un congrès
hal-04362506
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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
Communication dans un congrès
hal-03831746
v2
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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⟩
Communication dans un congrès
hal-04273997
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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
Communication dans un congrès
hal-04195756
v2
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From Nipype to Pydra: a Clinica storyOHBM 2023 - Annual meeting of the Organization for Human Brain Mapping, Jul 2023, Montreal, Canada
Communication dans un congrès
hal-04278898
v1
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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
Communication dans un congrès
hal-04185304
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How can data augmentation improve attribution maps for disease subtype explainability?SPIE Medical Imaging, Feb 2023, San Diego, United States
Communication dans un congrès
hal-03966737
v1
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Simulation-based evaluation framework for deep learning unsupervised anomaly detection on brain FDG PETSPIE Medical Imaging, Feb 2023, San Diego, United States
Communication dans un congrès
hal-03835015
v2
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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-03478798
v1
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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-03728243
v1
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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-04279014
v1
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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⟩
Communication dans un congrès
hal-03542213
v1
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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
Communication dans un congrès
hal-03513920
v1
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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
Communication dans un congrès
hal-02549242
v1
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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⟩
Communication dans un congrès
hal-02370532
v3
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Prediction of future cognitive scores and dementia onset in Mild Cognitive Impairment patientsOHBM 2019 - Organization for Human Brain Mapping Conference, Jun 2019, Rome, Italy
Communication dans un congrès
hal-02098427
v2
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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⟩
Communication dans un congrès
hal-02025880
v2
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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
Communication dans un congrès
hal-02105134
v2
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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
Communication dans un congrès
hal-02134909
v1
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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
Communication dans un congrès
hal-02105133
v2
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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
Communication dans un congrès
hal-02142315
v1
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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⟩
Communication dans un congrès
hal-02132147
v2
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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
Communication dans un congrès
hal-01757646
v1
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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
Communication dans un congrès
hal-01758206
v3
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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
Communication dans un congrès
hal-01760658
v1
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Three simple ideas for predicting progression to Alzheimer's disease8th International Workshop on Pattern Recognition in Neuroimaging, Jun 2018, Singapour, Singapore
Communication dans un congrès
hal-01891996
v1
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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
Communication dans un congrès
hal-01758167
v2
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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
Communication dans un congrès
hal-01761666
v1
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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⟩
Communication dans un congrès
hal-01632509
v1
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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⟩
Communication dans un congrès
hal-01827190
v1
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Brain volume, cerebral β-amyloid deposition, and ageing: A study of over 200 individuals born in the same week in 1946 AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P1464--P1465, ⟨10.1016/j.jalz.2017.07.534⟩
Communication dans un congrès
hal-01827188
v1
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Midlife affective symptoms are associated with lower brain volumes in later life: Evidence from a prospective UK birth cohort AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P212, ⟨10.1016/j.jalz.2017.07.086⟩
Communication dans un congrès
hal-01827192
v1
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A comparison of techniques for quantifying amyloid burden on a combined PET/MR scanner AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P12--P13, ⟨10.1016/j.jalz.2017.06.2276⟩
Communication dans un congrès
hal-01827194
v1
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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
Communication dans un congrès
hal-01567343
v1
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Exploring the population prevalence of β-amyloid burden: An analysis of 250 individuals born in mainland Britain in the same week in 1946 AAIC 2017 - Alzheimer's Association International Conference, Jul 2017, London, United Kingdom. pp.P1088--P1089, ⟨10.1016/j.jalz.2017.06.1563⟩
Communication dans un congrès
hal-01827189
v1
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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
Communication dans un congrès
hal-01827193
v1
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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⟩
Communication dans un congrès
hal-01621383
v1
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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
Communication dans un congrès
hal-01578479
v1
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CT synthesis in the head & neck and pelvic regions for radiotherapy treatment planningIPEM Workshop on MRI Guided Radiotherapy, Mar 2016, Sheffield, United Kingdom
Communication dans un congrès
hal-01827224
v1
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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
Communication dans un congrès
hal-01827200
v1
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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
Communication dans un congrès
hal-01827204
v1
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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
Communication dans un congrès
hal-01827199
v1
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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
Communication dans un congrès
hal-01827198
v1
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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⟩
Communication dans un congrès
hal-01827201
v1
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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
Communication dans un congrès
hal-01827203
v1
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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⟩
Communication dans un congrès
hal-01827208
v1
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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⟩
Communication dans un congrès
hal-01827209
v1
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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⟩
Communication dans un congrès
hal-01827216
v1
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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⟩
Communication dans un congrès
hal-01827212
v1
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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⟩
Communication dans un congrès
hal-01827205
v1
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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⟩
Communication dans un congrès
hal-01827210
v1
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Evaluation of different approaches to obtain synthetic CT images for a MRI-only radiotherapy workflowMR in RT symposium, Jun 2015, Lund, Sweden
Communication dans un congrès
hal-01827206
v1
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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⟩
Communication dans un congrès
hal-01827207
v1
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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⟩
Communication dans un congrès
hal-01827220
v1
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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⟩
Communication dans un congrès
hal-01827222
v1
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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⟩
Communication dans un congrès
hal-01827219
v1
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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⟩
Communication dans un congrès
hal-01827221
v1
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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⟩
Communication dans un congrès
hal-01827218
v1
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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⟩
Communication dans un congrès
istex
hal-01827223
v1
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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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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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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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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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Pseudo-healthy image reconstruction with variational autoencoders for anomaly detection: A benchmark on 3D brain FDG PET2024
Pré-publication, Document de travail
hal-04445378
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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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