| Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation J Wen, E Thibeau-Sutre, M Diaz-Melo, J Samper-González, A Routier, ... Medical image analysis 63, 101694, 2020 | 658 | 2020 |
| Clinica: An open-source software platform for reproducible clinical neuroscience studies A Routier, N Burgos, M Díaz, M Bacci, S Bottani, O El-Rifai, S Fontanella, ... Frontiers in Neuroinformatics 15, 689675, 2021 | 131 | 2021 |
| Predicting the progression of mild cognitive impairment using machine learning: a systematic, quantitative and critical review M Ansart, S Epelbaum, G Bassignana, A Bône, S Bottani, T Cattai, ... Medical Image Analysis 67, 101848, 2021 | 99 | 2021 |
| Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder C Chadebec, E Thibeau-Sutre, N Burgos, S Allassonnière IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (3), 2879-2896, 2022 | 88 | 2022 |
| Ensemble learning of convolutional neural network, support vector machine, and best linear unbiased predictor for brain age prediction: Aramis contribution to the predictive … B Couvy-Duchesne, J Faouzi, B Martin, E Thibeau–Sutre, A Wild, ... Frontiers in Psychiatry 11, 593336, 2020 | 41 | 2020 |
| ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing E Thibeau-Sutre, M Diaz, R Hassanaly, A Routier, D Dormont, O Colliot, ... Computer Methods and Programs in Biomedicine 220, 106818, 2022 | 39 | 2022 |
| Deep learning for brain disorders: from data processing to disease treatment N Burgos, S Bottani, J Faouzi, E Thibeau-Sutre, O Colliot Briefings in Bioinformatics 22 (2), 1560-1576, 2021 | 26 | 2021 |
| Australian Imaging Biomarkers and Lifestyle flagship study of ageing, Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation J Wen, E Thibeau-Sutre, M Diaz-Melo, J Samper-González, A Routier, ... Med. Image Anal 63, 101694, 2020 | 20* | 2020 |
| Visualization approach to assess the robustness of neural networks for medical image classification E Thibeau-Sutre, O Colliot, D Dormont, N Burgos Medical Imaging 2020: Image Processing 11313, 370-380, 2020 | 20 | 2020 |
| MRI field strength predicts Alzheimer’s disease: a case example of bias in the ADNI data set E Thibeau-Sutre, B Couvy-Duchesne, D Dormont, O Colliot, N Burgos 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-4, 2022 | 14 | 2022 |
| Interpretability of Machine Learning Methods Applied to Neuroimaging E Thibeau-Sutre, S Collin, N Burgos, O Colliot Machine Learning for Brain Disorders, 655-704, 2023 | 11 | 2023 |
| Homogenization of brain MRI from a clinical data warehouse using contrast-enhanced to non-contrast-enhanced image translation with U-Net derived models S Bottani, E Thibeau-Sutre, A Maire, S Ströer, D Dormont, O Colliot, ... Medical Imaging 2022: Image Processing 12032, 576-582, 2022 | 9 | 2022 |
| Reproducibility in machine learning for medical imaging O Colliot, E Thibeau-Sutre, N Burgos Machine Learning for Brain Disorders, 631-653, 2023 | 8 | 2023 |
| Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation. arXiv 2019 J Wen, E Thibeau-Sutre, J Samper-Gonzalez, A Routier, S Bottani, ... arXiv preprint arXiv:1904.07773, 2019 | 7 | 2019 |
| Visualization approach to assess the robustness of neural networks for medical image classification ET Sutre, O Colliot, D Dormont, N Burgos arXiv preprint arXiv:1911.08264, 2019 | 3 | 2019 |
| Reproducibility in medical image computing: What is it and how is it assessed? O Colliot, E Thibeau-Sutre, C Brianceau, N Burgos Trustworthy AI in Medical Imaging, 177-204, 2025 | 2 | 2025 |
| Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI E Thibeau-Sutre, D Alblas, S Buurman, C Brune, JM Wolterink International Workshop on Uncertainty for Safe Utilization of Machine …, 2023 | 2 | 2023 |
| How can data augmentation improve attribution maps for disease subtype explainability ET Sutre, JM Wolterink, O Colliot, N Burgos Medical Imaging 2023: Image Processing 12464, 497-503, 2023 | 2 | 2023 |
| Clinica: an open-source software platform for reproducible clinical neuroscience studies O El-Rifai, MD Melo, R Hassanaly, M Joulot, AM Routier, E Thibeau-Sutre, ... MRI Together 2021-A global workshop on Open Science and Reproducible MR Research, 2021 | 2 | 2021 |
| Reproducible and interpretable deep learning for the diagnosis, prognosis and subtyping of Alzheimer’s disease from neuroimaging data E Thibeau-Sutre Sorbonne université, 2021 | 2 | 2021 |
Ninon BurgosCNRS researcher - Paris Brain Institute (ICM), ARAMIS LabBestätigte E-Mail-Adresse bei cnrs.fr