
Ninon Burgos
118
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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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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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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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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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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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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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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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
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
v1
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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
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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