| A deep learning model to predict RNA-Seq expression of tumours from whole slide images B Schmauch, A Romagnoni, E Pronier, C Saillard, P Maillé, J Calderaro, ... Nature communications 11 (1), 1-15, 2020 | 303 | 2020 |
| Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides C Saillard, B Schmauch, O Laifa, M Moarii, S Toldo, M Zaslavskiy, ... Hepatology 72 (6), 2000-2013, 2020 | 205 | 2020 |
| Cnn+ lstm architecture for speech emotion recognition with data augmentation C Etienne, G Fidanza, A Petrovskii, L Devillers, B Schmauch arXiv preprint arXiv:1802.05630, 2018 | 129 | 2018 |
| 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 |
| Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer J Ogier du Terrail, A Leopold, C Joly, C Béguier, M Andreux, C Maussion, ... Nature medicine 29 (1), 135-146, 2023 | 66 | 2023 |
| Deep learning approach for diabetic retinopathy screening E Colas, A Besse, A Orgogozo, B Schmauch, N Meric, E Besse Acta Ophthalmologica 94, 2016 | 61 | 2016 |
| Linear growth of structure in the symmetron model P Brax, C van de Bruck, AC Davis, B Li, B Schmauch, DJ Shaw Physical Review D 84 (12), 123524, 2011 | 49 | 2011 |
| 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 |
| Flavour always matters in scalar triplet leptogenesis S Lavignac, B Schmauch Journal of High Energy Physics 2015 (5), 1-46, 2015 | 22 | 2015 |
| Speech emotion recognition with data augmentation and layer-wise learning rate adjustment C Etienne, G Fidanza, A Petrovskii, L Devillers, B Schmauch arXiv preprint arXiv:1802.05630 68, 2018 | 21 | 2018 |
| The physics of neutrinos RZ Funchal, B Schmauch, G Giesen arXiv preprint arXiv:1308.1029, 2013 | 18 | 2013 |
| Transcriptomic learning for digital pathology B Schmauch, A Romagnoni, E Pronier, C Saillard, P Maillé, J Calderaro, ... BioRxiv, 760173, 2019 | 10 | 2019 |
| Systems and methods for image preprocessing P Courtiol, O Moindrot, C Maussion, C Saillard, B Schmauch, G Wainrib US Patent 11,562,585, 2023 | 9 | 2023 |
| Collaborative federated learning behind hospitals’ firewalls for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer JO Du Terrail, A Leopold, C Joly, C Beguier, M Andreux, C Maussion, ... medRxiv, 2021.10. 27.21264834, 2021 | 9 | 2021 |
| 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 |
| 1124O Prediction of distant relapse in patients with invasive breast cancer from deep learning models applied to digital pathology slides IJ Garberis, C Saillard, D Drubay, B Schmauch, V Aubert, A Jaeger, ... Annals of Oncology 32, S921, 2021 | 7 | 2021 |
| Pacpaint: a histology-based deep learning model uncovers the extensive intratumor molecular heterogeneity of pancreatic adenocarcinoma C Saillard, F Delecourt, B Schmauch, O Moindrot, M Svrcek, ... Nature communications 14 (1), 3459, 2023 | 4 | 2023 |
| Deep learning allows assessment of risk of metastatic relapse from invasive breast cancer histological slides I Garberis, V Gaury, C Saillard, D Drubay, K Elgui, B Schmauch, A Jaeger, ... bioRxiv, 2022.11. 28.518158, 2022 | 3 | 2022 |
| HE2RNA: a deep learning model for transcriptomic learning from digital pathology E Pronier, B Schmauch, A Romagnoni, C Saillard, J Calderaro, M Sefta, ... Cancer Res 80, 2105-2105, 2020 | 3 | 2020 |
