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Elina Thibeau-Sutre
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2020 – today
- 2024
- [j7]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) - [c6]Baris Imre
, Elina Thibeau-Sutre, Jorieke Reimer, Kuan Kho, Jelmer M. Wolterink
Brain-Shift: Unsupervised Pseudo-Healthy Brain Synthesis for Novel Biomarker Extraction in Chronic Subdural Hematoma. MICCAI (2) 2024: 34-44 - [i7]Baris Imre, Elina Thibeau-Sutre, Jorieke Reimer, Kuan Kho, Jelmer M. Wolterink:
Brain-Shift: Unsupervised Pseudo-Healthy Brain Synthesis for Novel Biomarker Extraction in Chronic Subdural Hematoma. CoRR abs/2403.19415 (2024) - 2023
- [j6]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) - [c5]Elina Thibeau-Sutre, Dieuwertje Alblas, Sophie Buurman, Christoph Brune, Jelmer M. Wolterink:
Uncertainty-Based Quality Assurance of Carotid Artery Wall Segmentation in Black-Blood MRI. UNSURE@MICCAI 2023: 95-103 - [c4]Elina Thibeau-Sutre
How can data augmentation improve attribution maps for disease subtype explainability. Image Processing 2023 - [i6]Elina Thibeau-Sutre
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI. CoRR abs/2308.09538 (2023) - 2022
- [j5]Elina Thibeau-Sutre
ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing. Comput. Methods Programs Biomed. 220: 106818 (2022) - [c3]Elina Thibeau-Sutre
MRI Field Strength Predicts Alzheimer's Disease: a Case Example of Bias in the ADNI Data Set. ISBI 2022: 1-4 - [c2]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 - [i5]Elina Thibeau-Sutre
Interpretability of Machine Learning Methods Applied to Neuroimaging. CoRR abs/2204.07005 (2022) - [i4]Olivier Colliot
Reproducibility in machine learning for medical imaging. CoRR abs/2209.05097 (2022) - 2021
- [b1]Elina Thibeau-Sutre:
Reproducible and interpretable deep learning for the diagnosis, prognosis and subtyping of Alzheimer's disease from neuroimaging data. (Méthodes d'apprentissage profond reproductibles et interprétables pour le diagnostic, le pronostic et l'identification de sous-groupes de la maladie d'Alzheimer à partir de données de neuroimagerie). Sorbonne University, France, 2021 - [j4]Ninon Burgos
Deep learning for brain disorders: from data processing to disease treatment. Briefings Bioinform. 22(2): 1560-1576 (2021) - [j3]Alexandre Routier
Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies. Frontiers Neuroinformatics 15: 689675 (2021) - [j2]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) - [i3]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
- [j1]Junhao Wen
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Medical Image Anal. 63: 101694 (2020) - [c1]Elina Thibeau-Sutre
Visualization approach to assess the robustness of neural networks for medical image classification. Image Processing 2020: 113131J
2010 – 2019
- 2019
- [i2]Junhao Wen, Elina Thibeau-Sutre
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation. CoRR abs/1904.07773 (2019) - [i1]Elina Thibeau-Sutre
Visualization approach to assess the robustness of neural networks for medical image classification. CoRR abs/1911.08264 (2019)
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