| 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 | 159 | 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 |
| 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 | |
| An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data K Schutte, F Brulport, S Harguem-Zayani, JB Schiratti, R Ghermi, ... European Journal of Cancer 174, 90-98, 2022 | 7 | 2022 |
| Integration of clinical characteristics, lab tests and a deep learning CT scan analysis to predict severity of hospitalized COVID-19 patients N Lassau, S Ammari, E Chouzenoux, H Gortais, P Herent, M Devilder, ... MedRxiv, 2020 | 3 | 2020 |
| 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 |
| 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 |
| Development of a reproducible AI-based spatial biomarker of the tumor immune infiltrate on H&E slides V Di Proietto, J El-Khoury, B Adjadj, L Gillet, U Marteau, K Schutte, ... Cancer Research 84 (6_Supplement), 7399-7399, 2024 | | 2024 |
| Deep learning with whole slides images to predict histological response to neoadjuvant chemotherapy in patients with resectable high grade soft-tissue sarcomas: A multicenter … B Adjadj, K Schutte, C Maussion, M Jean-Denis, M Karanian, JM Coindre, ... Journal of Clinical Oncology 41 (16_suppl), 11512-11512, 2023 | | 2023 |
| Intra-tumor heterogeneity assessment of liver lesions for colorectal cancer patients on CT-scans R Ghermi, S Ammari, G Garcia, S Harguem-zayani, N Lassau, K Schutte Cancer Research 83 (7_Supplement), 3316-3316, 2023 | | 2023 |
| Multimodal prediction of metastatic relapse using federated deep learning outperforms state-of-the-art methods in soft-tissue sarcoma C Maussion, JM Coindre, JY Blay, K Schutte, A Camara, FL Loarer, ... Cancer Research 82 (12_Supplement), 1939-1939, 2022 | | 2022 |
| PULS-AI: A multimodal artificial intelligence model to predict survival of solid tumor patients treated with antiangiogenics K Schutte, F Brulport, S Harguem-Zayani, JB Schiratti, R Ghermi, ... Cancer Research 82 (12_Supplement), 1924-1924, 2022 | | 2022 |
| 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 |
