| The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation S Jégou, M Drozdzal, D Vazquez, A Romero, Y Bengio Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1983 | 2017 |
| 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 | 171 | 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 | 124 | 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 | 111 | 2019 |
| Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data A Romagnoni, S Jégou, K Van Steen, G Wainrib, JP Hugot Scientific reports 9 (1), 10351, 2019 | 90 | 2019 |
| Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network. P Blanc-Durand, S Jégou, S Kanoun, A Berriolo-Riedinger, C Bodet-Milin, ... European Journal of Nuclear Medicine and Molecular Imaging 48, 1362-1370, 2021 | 84 | 2021 |
| Using stylegan for visual interpretability of deep learning models on medical images K Schutte, O Moindrot, P Hérent, JB Schiratti, S Jégou arXiv preprint arXiv:2101.07563, 2021 | 52 | 2021 |
| Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment P Blanc-Durand, JB Schiratti, K Schutte, P Jehanno, P Herent, F Pigneur, ... Diagnostic and Interventional Imaging 101 (12), 789-794, 2020 | 38 | 2020 |
| 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 | 36 | 2021 |
| Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer P Blanc-Durand, L Campedel, S Mule, S Jegou, A Luciani, F Pigneur, E Itti European radiology 30, 3528-3537, 2020 | 34 | 2020 |
| ToxicBlend: Virtual screening of toxic compounds with ensemble predictors M Zaslavskiy, S Jégou, EW Tramel, G Wainrib Computational Toxicology 10, 81-88, 2019 | 21 | 2019 |
| Image segmentation by iterative inference from conditional score estimation A Romero, M Drozdzal, A Erraqabi, S Jégou, Y Bengio arXiv preprint arXiv:1705.07450, 2017 | 15 | 2017 |
| Brain age prediction of healthy subjects on anatomic MRI with deep learning: Going beyond with an “explainable AI” mindset P Herent, S Jegou, G Wainrib, T Clozel BioRxiv, 413302, 2018 | 11 | 2018 |
| General image descriptors for open world image retrieval using vit clip MV Conde, I Aerlic, S Jégou arXiv preprint arXiv:2210.11141, 2022 | 3 | 2022 |
| 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 | 2 | 2023 |
| Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks D Said, G Carbonell, D Stocker, S Hectors, N Vietti-Violi, O Bane, X Chin, ... European Radiology 33 (9), 6020-6032, 2023 | 2 | 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 |
| The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data LH Boulogne, J Lorenz, D Kienzle, R Schon, K Ludwig, R Lienhart, ... arXiv preprint arXiv:2306.10484, 2023 | | 2023 |
| Dissociated response as a new biomarker of treatment response in neuroendocrine tumors: Results from phase 3 of the RAISE project C Dromain, M Pavel, M Ronot, N Schaefer, D Mandair, D Gueguen, ... JOURNAL OF NEUROENDOCRINOLOGY 33, 119-119, 2021 | | 2021 |
| ToxicBlend: Virtual screening of toxic compound with ensemble predictors M Zaslayskiy, S Jegou, EW Tramel, G Wainrib TOXICOLOGY LETTERS 295, S98-S98, 2018 | | 2018 |
