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Ninon Burgos
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2020 – today
- 2024
- [j18]Simona Bottani, Elina Thibeau-Sutre, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos:
Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI. BMC Medical Imaging 24(1): 67 (2024) - [j17]Sophie Loizillon
, Simona Bottani, Aurélien Maire
Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data. Medical Image Anal. 93: 103073 (2024) - [c23]Evangelia Christodoulou, Annika Reinke, Rola Houhou, Piotr Kalinowski, Selen Erkan, Carole H. Sudre, Ninon Burgos, Sofiène Boutaj, Sophie Loizillon, Maëlys Solal, Nicola Rieke, Veronika Cheplygina, Michela Antonelli, Leon D. Mayer, Minu Dietlinde Tizabi, M. Jorge Cardoso, Amber L. Simpson, Paul F. Jäger, Annette Kopp-Schneider, Gaël Varoquaux, Olivier Colliot, Lena Maier-Hein:
Confidence Intervals Uncovered: Are We Ready for Real-World Medical Imaging AI? MICCAI (10) 2024: 124-132 - [i15]Ravi Hassanaly, Camille Brianceau, Maëlys Solal, Olivier Colliot, Ninon Burgos:
Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET. CoRR abs/2401.16363 (2024) - [i14]Sophie Loizillon, Simona Bottani, Stéphane Mabille, Yannick Jacob, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos:
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation. CoRR abs/2406.12448 (2024) - 2023
- [j16]Simona Bottani
Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse. Medical Image Anal. 89: 102903 (2023) - [j15]Clément Chadebec
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder. IEEE Trans. Pattern Anal. Mach. Intell. 45(3): 2879-2896 (2023) - [c22]Francesco Galati, Daniele Falcetta, Rosa Cortese, Barbara Casolla, Ferran Prados, Ninon Burgos, Maria A. Zuluaga:
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation. BMVC 2023: 750-751 - [c21]Sophie Loizillon, Olivier Colliot, Lydia Chougar, Sebastian Ströer, Yannick Jacob, Aurélien Maire, Didier Dormont, Ninon Burgos:
Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data Warehouse. DART@MICCAI 2023: 84-93 - [c20]Ravi Hassanaly, Camille Brianceau, Olivier Colliot, Ninon Burgos:
Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches. DGM4MICCAI 2023: 110-120 - [c19]Ninon Burgos:
Exploiting hospital data warehouses: the challenges of image quality and heterogeneity (Conference Presentation). Image Processing 2023 - [c18]Ravi Hassanaly, Simona Bottani, Benoît Sauty, Olivier Colliot, Ninon Burgos:
Simulation based evaluation framework for deep learning unsupervised anomaly detection on brain FDG-PET. Image Processing 2023 - [c17]Sophie Loizillon, Simona Bottani, Aurélien Maire, Sebastian Ströer, Didier Dormont, Olivier Colliot, Ninon Burgos:
Transfer learning from synthetic to routine clinical data for motion artefact detection in brain T1-weighted MRI. Image Processing 2023 - [c16]Elina Thibeau-Sutre
How can data augmentation improve attribution maps for disease subtype explainability. Image Processing 2023 - [i13]Guanghui Fu, Gabriel Jimenez, Sophie Loizillon, Lydia Chougar, Didier Dormont, Romain Valabrègue, Ninon Burgos, Stéphane Lehéricy, Daniel Racoceanu, Olivier Colliot:
Frequency Disentangled Learning for Segmentation of Midbrain Structures from Quantitative Susceptibility Mapping Data. CoRR abs/2302.12980 (2023) - [i12]Francesco Galati, Daniele Falcetta, Rosa Cortese, Barbara Casolla, Ferran Prados, Ninon Burgos, Maria A. Zuluaga:
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation. CoRR abs/2309.06075 (2023) - [i11]Maëlys Solal, Ravi Hassanaly, Ninon Burgos:
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET. CoRR abs/2311.12081 (2023) - 2022
- [b1]Ninon Burgos:
Individualised, interpretable and reproducible computer-aided diagnosis of dementia: towards application in clinical practice. Sorbonne University, Paris, France, 2022 - [j14]Elina Thibeau-Sutre
ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing. Comput. Methods Programs Biomed. 220: 106818 (2022) - [j13]Simona Bottani, Ninon Burgos
Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse. Medical Image Anal. 75: 102219 (2022) - [c15]Elina Thibeau-Sutre
MRI Field Strength Predicts Alzheimer's Disease: a Case Example of Bias in the ADNI Data Set. ISBI 2022: 1-4 - [c14]Simona Bottani, Elina Thibeau-Sutre
Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation with U-Net derived models. Image Processing 2022 - [i10]Elina Thibeau-Sutre
Interpretability of Machine Learning Methods Applied to Neuroimaging. CoRR abs/2204.07005 (2022) - [i9]Olivier Colliot
Reproducibility in machine learning for medical imaging. CoRR abs/2209.05097 (2022) - [i8]Guanghui Fu, Gabriel Jimenez, Sophie Loizillon, Rosana El Jurdi, Lydia Chougar, Didier Dormont, Romain Valabrègue, Ninon Burgos, Stéphane Lehéricy, Daniel Racoceanu, Olivier Colliot
Fourier Disentangled Multimodal Prior Knowledge Fusion for Red Nucleus Segmentation in Brain MRI. CoRR abs/2211.01353 (2022) - 2021
- [j12]Ninon Burgos
Deep learning for brain disorders: from data processing to disease treatment. Briefings Bioinform. 22(2): 1560-1576 (2021) - [j11]Alexandre Routier
Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies. Frontiers Neuroinformatics 15: 689675 (2021) - [j10]Manon Ansart, Stéphane Epelbaum
Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Medical Image Anal. 67: 101848 (2021) - [j9]Junhao Wen
Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease. Neuroinformatics 19(1): 57-78 (2021) - [e4]David Svoboda
Simulation and Synthesis in Medical Imaging - 6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Lecture Notes in Computer Science 12965, Springer 2021, ISBN 978-3-030-87591-6 [contents] - [i7]Simona Bottani, Ninon Burgos, Aurélien Maire, Adam Wild, Sebastian Ströer, Didier Dormont, Olivier Colliot:
Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse. CoRR abs/2104.08131 (2021) - [i6]Clément Chadebec, Elina Thibeau-Sutre, Ninon Burgos, Stéphanie Allassonnière:
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder. CoRR abs/2105.00026 (2021) - 2020
- [j8]Junhao Wen
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Medical Image Anal. 63: 101694 (2020) - [c13]Elina Thibeau-Sutre
Visualization approach to assess the robustness of neural networks for medical image classification. Image Processing 2020: 113131J - [e3]Ninon Burgos
Simulation and Synthesis in Medical Imaging - 5th International Workshop, SASHIMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings. Lecture Notes in Computer Science 12417, Springer 2020, ISBN 978-3-030-59519-7 [contents]
2010 – 2019
- 2019
- [c12]Jorge Samper-González, Ninon Burgos
Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data. Image Processing 2019: 109490V - [e2]Ninon Burgos
Simulation and Synthesis in Medical Imaging - 4th International Workshop, SASHIMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings. Lecture Notes in Computer Science 11827, Springer 2019, ISBN 978-3-030-32777-4 [contents] - [i5]Junhao Wen, Elina Thibeau-Sutre
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation. CoRR abs/1904.07773 (2019) - [i4]Elina Thibeau-Sutre
Visualization approach to assess the robustness of neural networks for medical image classification. CoRR abs/1911.08264 (2019) - 2018
- [j7]Arnaud Marcoux, Ninon Burgos
An Automated Pipeline for the Analysis of PET Data on the Cortical Surface. Frontiers Neuroinformatics 12: 94 (2018) - [j6]Jorge Samper-González, Ninon Burgos
Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data. NeuroImage 183: 504-521 (2018) - [e1]Ali Gooya
Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Lecture Notes in Computer Science 11037, Springer 2018, ISBN 978-3-030-00535-1 [contents] - [i3]Jorge Samper-González, Ninon Burgos, Simona Bottani, Sabrina Fontanella, Pascal Lu, Arnaud Marcoux, Alexandre Routier, Jérémy Guillon, Michael Bacci, Junhao Wen, Anne Bertrand, Hugo Bertin, Marie Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot, Alzheimer's Disease Neuroimaging Initiative:
Reproducible evaluation of classification methods in Alzheimer's disease: framework and application to MRI and PET data. CoRR abs/1808.06452 (2018) - [i2]Junhao Wen, Jorge Samper-González, Simona Bottani, Alexandre Routier, Ninon Burgos
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease. CoRR abs/1812.11183 (2018) - 2017
- [j5]Claes Nøhr Ladefoged
A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients. NeuroImage 147: 346-359 (2017) - [j4]Jieqing Jiao, Alexandre Bousse, Kris Thielemans
Direct Parametric Reconstruction With Joint Motion Estimation/Correction for Dynamic Brain PET Data. IEEE Trans. Medical Imaging 36(1): 203-213 (2017) - [c11]Ninon Burgos
Individual Analysis of Molecular Brain Imaging Data Through Automatic Identification of Abnormality Patterns. CMMI/RAMBO/SWITCH@MICCAI 2017: 13-22 - [c10]Jorge Samper-González, Ninon Burgos
Yet Another ADNI Machine Learning Paper? Paving the Way Towards Fully-Reproducible Research on Classification of Alzheimer's Disease. MLMI@MICCAI 2017: 53-60 - [c9]Catherine J. Scott, Jieqing Jiao, M. Jorge Cardoso, Andrew Melbourne, Enrico De Vita, David Thomas
Short Acquisition Time PET Quantification Using MRI-Based Pharmacokinetic Parameter Synthesis. MICCAI (2) 2017: 737-744 - [i1]Jorge Samper-González, Ninon Burgos, Sabrina Fontanella, Hugo Bertin, Marie Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot:
Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease. CoRR abs/1709.07267 (2017) - 2016
- [c8]Ninon Burgos
Joint Segmentation and CT Synthesis for MRI-only Radiotherapy Treatment Planning. MICCAI (2) 2016: 547-555 - 2015
- [j3]Martin Kochan, Pankaj Daga, Ninon Burgos
Simulated field maps for susceptibility artefact correction in interventional MRI. Int. J. Comput. Assist. Radiol. Surg. 10(9): 1405-1416 (2015) - [j2]Maria A. Zuluaga
Voxelwise atlas rating for computer assisted diagnosis: Application to congenital heart diseases of the great arteries. Medical Image Anal. 26(1): 185-194 (2015) - [c7]Jieqing Jiao, Pawel Markiewicz, Ninon Burgos
Detail-Preserving PET Reconstruction with Sparse Image Representation and Anatomical Priors. IPMI 2015: 540-551 - [c6]Maria A. Zuluaga
Multi-atlas synthesis for computer assisted diagnosis: Application to cardiovascular diseases. ISBI 2015: 290-293 - [c5]Ninon Burgos
Robust CT Synthesis for Radiotherapy Planning: Application to the Head and Neck Region. MICCAI (2) 2015: 476-484 - [c4]Ninon Burgos
Subject-specific Models for the Analysis of Pathological FDG PET Data. MICCAI (2) 2015: 651-658 - 2014
- [j1]Ninon Burgos
Attenuation Correction Synthesis for Hybrid PET-MR Scanners: Application to Brain Studies. IEEE Trans. Medical Imaging 33(12): 2332-2341 (2014) - [c3]Martin Kochan, Pankaj Daga, Ninon Burgos
Simulated Field Maps: Toward Improved Susceptibility Artefact Correction in Interventional MRI. IPCAI 2014: 226-235 - [c2]Jieqing Jiao, Alexandre Bousse
Joint Parametric Reconstruction and Motion Correction Framework for Dynamic PET Data. MICCAI (1) 2014: 114-121 - 2013
- [c1]Ninon Burgos
Attenuation Correction Synthesis for Hybrid PET-MR Scanners. MICCAI (1) 2013: 147-154
Coauthor Index
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last updated on 2024-11-28 20:34 CET by the dblp team
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