| Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes E Kiner, E Willie, B Vijaykumar, K Chowdhary, H Schmutz, J Chandler, ... Nature immunology 22 (2), 216-228, 2021 | 134 | 2021 |
| Deep learning of immune cell differentiation A Maslova, RN Ramirez, K Ma, H Schmutz, C Wang, C Fox, B Ng, ... Proceedings of the National Academy of Sciences 117 (41), 25655-25666, 2020 | 73 | 2020 |
| Model-agnostic out-of-distribution detection using combined statistical tests F Bergamin, PA Mattei, JD Havtorn, H Senetaire, H Schmutz, L Maaløe, ... International Conference on Artificial Intelligence and Statistics, 10753-10776, 2022 | 12 | 2022 |
| Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F] FDOPA PET/CT V Comte, H Schmutz, D Chardin, F Orlhac, J Darcourt, O Humbert European Journal of Nuclear Medicine and Molecular Imaging 49 (11), 3787-3796, 2022 | 7 | 2022 |
| Don't fear the unlabelled: safe deep semi-supervised learning via simple debiasing H Schmutz, O Humbert, PA Mattei 11th International Conference of Learning Representations, 2023 | 5* | 2023 |
| Immunological Genome Project A Maslova, RN Ramirez, K Ma, H Schmutz, C Wang, C Fox, B Ng, ... Deep learning of immune cell differentiation. Proc. Natl. Acad. Sci. USA 117 …, 2020 | 5 | 2020 |
| Don’t fear the unlabelled: safe semi-supervised learning via debiasing H Schmutz, O Humbert, PA Mattei The Eleventh International Conference on Learning Representations, 2022 | 4 | 2022 |
| Learning immune cell differentiation A Maslova, RN Ramirez, K Ma, H Schmutz, C Wang, C Fox, B Ng, ... BioRxiv, 2019.12. 21.885814, 2019 | 4 | 2019 |
| Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. A Sportisse, H Schmutz, O Humbert, C Bouveyron, PA Mattei International Conference on Machine Learning, 32521-32539, 2023 | 2 | 2023 |
| Publisher Correction: Gut CD4+ T cell phenotypes are a continuum molded by microbes, not by TH archetypes E Kiner, E Willie, B Vijaykumar, K Chowdhary, H Schmutz, J Chandler, ... Nature Immunology 22 (5), 666-668, 2021 | 1 | 2021 |
| Apprentissage semi-supervisé, segmentation d'images TEP/TDM et prédiction de la réponse tumorale à l'immunothérapie| Theses. fr H Schmutz Université Côte d'Azur, 2023 | | 2023 |
| Semi-supervised learning, PET/CT segmentation and prediction of the tumoral response to immunotherapy H Schmutz Université Côte d'Azur, 2023 | | 2023 |
| Predicting non-small cell lung cancer response to immune checkpoint inhibitors with machine learning based on heterogeneous biomarkers. H Schmutz, PA Mattei, P Tricarico, S Contu, F Hugonnet, F Guisier, ... Journal of Clinical Oncology 41 (16_suppl), e21068-e21068, 2023 | | 2023 |
| 18FDG PET/CT and Machine Learning for the prediction of lung cancer response to immunotherapy H Schmutz, PA Mattei, S Contu, D Chardin, O Humbert EANM 2022-35th Annual Congres-Annual Congress of the European Association of …, 2022 | | 2022 |
Pierre-Alexandre MatteiResearch scientist, InriaVerified email at inria.fr
