| The importance of skip connections in biomedical image segmentation M Drozdzal, E Vorontsov, G Chartrand, S Kadoury, C Pal Deep learning and data labeling for medical applications, 179-187, 2016 | 826 | 2016 |
| Deep learning: a primer for radiologists G Chartrand, PM Cheng, E Vorontsov, M Drozdzal, S Turcotte, CJ Pal, ... Radiographics 37 (7), 2113-2131, 2017 | 618 | 2017 |
| The liver tumor segmentation benchmark (lits) P Bilic, PF Christ, E Vorontsov, G Chlebus, H Chen, Q Dou, CW Fu, X Han, ... arXiv preprint arXiv:1901.04056, 2019 | 384 | 2019 |
| Learning normalized inputs for iterative estimation in medical image segmentation M Drozdzal, G Chartrand, E Vorontsov, M Shakeri, L Di Jorio, A Tang, ... Medical image analysis 44, 1-13, 2018 | 214 | 2018 |
| Liver segmentation: indications, techniques and future directions A Gotra, L Sivakumaran, G Chartrand, KN Vu, F Vandenbroucke-Menu, ... Insights into imaging 8 (4), 377-392, 2017 | 123 | 2017 |
| Effects of insulin glargine and liraglutide therapy on liver fat as measured by magnetic resonance in patients with type 2 diabetes: a randomized trial A Tang, R Rabasa-Lhoret, H Castel, C Wartelle-Bladou, G Gilbert, ... Diabetes Care 38 (7), 1339-1346, 2015 | 100 | 2015 |
| Liver segmentation on CT and MR using Laplacian mesh optimization G Chartrand, T Cresson, R Chav, A Gotra, A Tang, JA De Guise IEEE Transactions on Biomedical Engineering 64 (9), 2110-2121, 2016 | 59 | 2016 |
| Learning to learn with conditional class dependencies X Jiang, M Havaei, F Varno, G Chartrand, N Chapados, S Matwin international conference on learning representations, 2018 | 45 | 2018 |
| MRI‐determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes KN Vu, G Gilbert, M Chalut, M Chagnon, G Chartrand, A Tang Journal of Magnetic Resonance Imaging 43 (5), 1090-1099, 2016 | 39 | 2016 |
| Semi-automated liver CT segmentation using Laplacian meshes G Chartrand, T Cresson, R Chav, A Gotra, A Tang, J DeGuise 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 641-644, 2014 | 32 | 2014 |
| Validation of a semiautomated liver segmentation method using CT for accurate volumetry A Gotra, G Chartrand, K Massicotte-Tisluck, F Morin-Roy, ... Academic radiology 22 (9), 1088-1098, 2015 | 20 | 2015 |
| Comparison of MRI-and CT-based semiautomated liver segmentation: a validation study A Gotra, G Chartrand, KN Vu, F Vandenbroucke-Menu, ... Abdominal radiology 42 (2), 478-489, 2017 | 17 | 2017 |
| [18F]-fluorodeoxyglucose positron emission tomography of the cat brain: a feasibility study to investigate osteoarthritis-associated pain M Guillot, G Chartrand, R Chav, J Rousseau, JF Beaudoin, ... The Veterinary Journal 204 (3), 299-303, 2015 | 17 | 2015 |
| Attentive task-agnostic meta-learning for few-shot text classification X Jiang, M Havaei, G Chartrand, H Chouaib, T Vincent, A Jesson, ... | 13 | 2018 |
| On the importance of attention in meta-learning for few-shot text classification X Jiang, M Havaei, G Chartrand, H Chouaib, T Vincent, A Jesson, ... arXiv preprint arXiv:1806.00852, 2018 | 11 | 2018 |
| Kidney segmentation from a single prior shape in MRI R Chav, T Cresson, G Chartrand, C Kauffmann, G Soulez, JA de Guise 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 818-821, 2014 | 10 | 2014 |
| Deep learning: an update for radiologists PM Cheng, E Montagnon, R Yamashita, I Pan, A Cadrin-Chênevert, ... Radiographics 41 (5), 1427-1445, 2021 | 9 | 2021 |
| 3D knee segmentation based on three MRI sequences from different planes L Zhou, R Chav, T Cresson, G Chartrand, J De Guise 2016 38th Annual International Conference of the IEEE Engineering in …, 2016 | 6 | 2016 |
| Accuracy of a semi-automated liver segmentation method using CT scan G Chartrand, C Belanger, A Gotra, R Chav, C Kauffman, JA De Guise, ... European Congress of Radiology-ECR 2013, 2013 | 3 | 2013 |
| Segmentation 3D du foie G Chartrand École de technologie supérieure, 2017 | 2 | 2017 |
An Tang, MD, MSc, FRCPCProfessor of Radiology, University of MontrealDirección de correo verificada de umontreal.ca
