| 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 | 28 | 2019 |
| 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, 2020 | 23 | 2020 |
| 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 | 23 | 2019 |
| 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 | 9 | 2020 |
| Transcriptomic learning for digital pathology B Schmauch, A Romagnoni, E Pronier, C Saillard, P Maillé, J Calderaro, ... bioRxiv, 760173, 2019 | 4 | 2019 |
| Reply to Zhen et al. Letter to the Editor J Calderaro, B Schmauch, C Saillard, P Courtiol Hepatology, 2020 | | 2020 |
| HE2RNA: A deep learning model for transcriptomic learning from digital pathology E Pronier, B Schmauch, A Romagnoni, C Saillard, J Calderaro, M Sefta, ... Cancer Research 80 (16 Supplement), 2105-2105, 2020 | | 2020 |
| Federated Survival Analysis with Discrete-Time Cox Models M Andreux, A Manoel, R Menuet, C Saillard, C Simpson arXiv preprint arXiv:2006.08997, 2020 | | 2020 |
