| Starcoder: may the source be with you! R Li, LB Allal, Y Zi, N Muennighoff, D Kocetkov, C Mou, M Marone, C Akiki, ... arXiv preprint arXiv:2305.06161, 2023 | 530 | 2023 |
| Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients N Lassau, S Ammari, E Chouzenoux, H Gortais, P Herent, M Devilder, ... Nature communications 12 (1), 1-11, 2021 | 173 | 2021 |
| Diagnosis of focal liver lesions from ultrasound using deep learning B Schmauch, P Herent, P Jehanno, O Dehaene, C Saillard, C Aubé, ... Diagnostic and interventional imaging 100 (4), 227-233, 2019 | 133 | 2019 |
| Detection and characterization of MRI breast lesions using deep learning P Herent, B Schmauch, P Jehanno, O Dehaene, C Saillard, C Balleyguier, ... Diagnostic and interventional imaging 100 (4), 219-225, 2019 | 117 | 2019 |
| Starcoder 2 and the stack v2: The next generation A Lozhkov, R Li, LB Allal, F Cassano, J Lamy-Poirier, N Tazi, A Tang, ... arXiv preprint arXiv:2402.19173, 2024 | 76 | 2024 |
| Self-supervision closes the gap between weak and strong supervision in histology O Dehaene, A Camara, O Moindrot, A de Lavergne, P Courtiol arXiv preprint arXiv:2012.03583, 2020 | 76 | 2020 |
| Danish Contractor R Li, LB Allal, Y Zi, N Muennighoff, D Kocetkov, C Mou, M Marone, C Akiki, ... Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz …, 2023 | 74 | 2023 |
| Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries R Li, LB Allal, Y Zi, N Muennighoff, D Kocetkov, C Mou, M Marone, C Akiki, ... Starcoder: may the source be with you 3 (3.5), 1, 2023 | 63 | 2023 |
| Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers C Saillard, O Dehaene, T Marchand, O Moindrot, A Kamoun, B Schmauch, ... arXiv preprint arXiv:2109.05819, 2021 | 40 | 2021 |
| Automatic construction of a phonics curriculum for reading education using the transformer neural network C Potier Watkins, O Dehaene, S Dehaene Artificial Intelligence in Education: 20th International Conference, AIED …, 2019 | 14 | 2019 |
| AI-based multi-modal integration of clinical characteristics, lab tests and chest CTs improves COVID-19 outcome prediction of hospitalized patients N Lassau, S Ammari, E Chouzenoux, H Gortais, P Herent, M Devilder, ... medRxiv, 2020.05. 14.20101972, 2020 | 9 | 2020 |
| Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun. 2021; 12: 634 N Lassau, S Ammari, E Chouzenoux, H Gortais, P Herent, M Devilder, ... | 8 | |
| The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors M Pavel, C Dromain, M Ronot, N Schaefer, D Mandair, D Gueguen, ... Future Oncology 19 (32), 2185-2199, 2023 | 3 | 2023 |
| Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors C Dromain, M Pavel, M Ronot, N Schaefer, D Mandair, D Gueguen, ... Future Oncology 19 (32), 2171-2183, 2023 | 1 | 2023 |
| Systems and methods for determining regions of interest in histology images O Dehaene, A Camara, O Moindrot, P Courtiol US Patent App. 18/254,701, 2024 | | 2024 |
| The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors: Results from phase 3 of the RAISE project M Pavel, C Dromain, M Ronot, N Schaefer, D Mandair, D Gueguen, ... JOURNAL OF NEUROENDOCRINOLOGY 33, 126-126, 2021 | | 2021 |
