| The importance of skip connections in biomedical image segmentation M Drozdzal, E Vorontsov, G Chartrand, S Kadoury, C Pal International Workshop on Deep Learning in Medical Image Analysis …, 2016 | 1259 | 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 | 1045 | 2017 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 933 | 2023 |
| A large annotated medical image dataset for the development and evaluation of segmentation algorithms AL Simpson, M Antonelli, S Bakas, M Bilello, K Farahani, B van Ginneken, ... arXiv preprint arXiv:1902.09063, 2019 | 903 | 2019 |
| The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature Communications 13 (1), 1-13, 2022 | 643 | 2022 |
| 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, 2017 | 265 | 2017 |
| On orthogonality and learning recurrent networks with long term dependencies E Vorontsov, C Trabelsi, S Kadoury, C Pal Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 247 | 2017 |
| Liver lesion segmentation informed by joint liver segmentation E Vorontsov, A Tang, C Pal, S Kadoury 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018 | 166 | 2018 |
| Deep learning and data labeling for medical applications M Drozdzal, E Vorontsov, G Chartrand, S Kadoury, C Pal LNCS 10008, 179-187, 2016 | 127* | 2016 |
| Deep learning for automated segmentation of liver lesions at CT in patients with colorectal cancer liver metastases E Vorontsov, M Cerny, P Régnier, L Di Jorio, CJ Pal, R Lapointe, ... Radiology: Artificial Intelligence 1 (2), 180014, 2019 | 106 | 2019 |
| Towards non-saturating recurrent units for modelling long-term dependencies S Chandar, C Sankar, E Vorontsov, SE Kahou, Y Bengio Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3280-3287, 2019 | 69 | 2019 |
| Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics G Kerg, K Goyette, MP Touzel, G Gidel, E Vorontsov, Y Bengio, G Lajoie Advances in Neural Information Processing Systems, 13613-13623, 2019 | 68 | 2019 |
| Dynamics and Distribution of Klothoβ (KLB) and Fibroblast Growth Factor Receptor-1 (FGFR1) in Living Cells Reveal the Fibroblast Growth Factor-21 (FGF21)-induced Receptor Complex AYK Ming, E Yoo, EN Vorontsov, SM Altamentova, DM Kilkenny, ... Journal of Biological Chemistry 287 (24), 19997-20006, 2012 | 52 | 2012 |
| Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models E Vorontsov, N Abi-Jaoudeh, S Kadoury Abdominal Imaging. Computational and Clinical Applications: 6th …, 2014 | 43 | 2014 |
| Metastatic liver tumour segmentation from discriminant Grassmannian manifolds S Kadoury, E Vorontsov, A Tang Physics in Medicine & Biology 60 (16), 6459, 2015 | 38 | 2015 |
| Towards semi-supervised segmentation via image-to-image translation E Vorontsov, P Molchanov, C Beckham, W Byeon, S De Mello, V Jampani, ... arXiv preprint arXiv:1904.01636, 2019 | 33* | 2019 |
| Metastatic liver tumour segmentation with a neural network-guided 3D deformable model E Vorontsov, A Tang, D Roy, CJ Pal, S Kadoury Medical & biological engineering & computing, 1-13, 2016 | 31 | 2016 |
| Virchow: A million-slide digital pathology foundation model E Vorontsov, A Bozkurt, A Casson, G Shaikovski, M Zelechowski, S Liu, ... arXiv preprint arXiv:2309.07778, 2023 | 21 | 2023 |
| Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks WT Le, E Vorontsov, FP Romero, L Seddik, MM Elsharief, PF Nguyen-Tan, ... Scientific Reports 12 (1), 3183, 2022 | 17 | 2022 |
| Label noise in segmentation networks: mitigation must deal with bias E Vorontsov, S Kadoury Deep Generative Models, and Data Augmentation, Labelling, and Imperfections …, 2021 | 17 | 2021 |
