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Alefsen, M., Vorontsov, E., & Kadoury, S. (octobre 2023). M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision [Communication écrite]. 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023), Vancouver, BC, Canada. Lien externe
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., van Ginneken, B., Bilello, M., Bilic, P., Christ, P. F., Do, R. K. G., Gollub, M. J., Heckers, S. H., Huisman, H., Jarnagin, W. R., ... Cardoso, M. J. (2022). The medical segmentation decathlon. Nature Communications, 13(1), 4128 (13 pages). Disponible
Anbil Parthipan, S. C., Sankar, C., Vorontsov, E., Kahou, S. E., & Bengio, Y. (2019). Towards Non-Saturating Recurrent Units for Modelling Long-Term Dependencies. AAAI Conference on Artificial Intelligence, 33(1), 3280-3287. Lien externe
Bilic, P., Christ, P., Li, H. B., Vorontsov, E., Ben-Cohen, A., Kaissis, G., Szeskin, A., Jacobs, C., Mamani, G. E. H., Chartrand, G., Lohöfer, F., Holch, J. W., Sommer, W., Hofmann, F., Hostettler, A., Lev-Cohain, N., Drozdzal, M., Amitai, M. M., Vivanti, R., ... Menze, B. (2023). The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis, 84, 102680 (24 pages). Lien externe
Cros, S., Bouttier, H., Nguyen-Tan, P. F., Vorontsov, E., & Kadoury, S. (2022). Combining dense elements with attention mechanisms for 3D radiotherapy dose prediction on head and neck cancers. Journal of Applied Clinical Medical Physics, 23(8), e13655 (15 pages). Lien externe
Cros, S., Vorontsov, E., & Kadoury, S. (avril 2021). Managing Class Imbalance in Multi-Organ CT Segmentation in Head and Neck Cancer Patients [Communication écrite]. 18th IEEE International Symposium on Biomedical Imaging (ISBI 2021), Nice, France. Lien externe
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017). Deep Learning: A Primer for Radiologists. RadioGraphics, 37(7), 2113-2131. Lien externe
Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., Romero, A., Bengio, Y., Pal, C. J., & Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical Image Analysis, 44, 1-13. Lien externe
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., & Pal, C. J. (octobre 2016). The Importance of Skip Connections in Biomedical Image Segmentation [Communication écrite]. 2nd International Workshop on Deep Learning in Medical Image Analysis (DLMIA 2016), held in conjunction with MICCAI 2016, Athens, Greece. Lien externe
Kerg, G., Goyette, K., Touzel, M. P., Gidel, G., Vorontsov, E., Bengio, Y., & Lajoie, G. (décembre 2019). Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics [Communication écrite]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, B.-C.. Lien externe
Le, W. T., Vorontsov, E., Romero, F. P., Seddik, L., Elsharief, M. M., Nguyen-Tan, P. F., Roberge, D., Bahig, H., & Kadoury, S. (2022). Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks. Scientific Reports, 12(1), 17 pages. Lien externe
Vorontsov, E., Molchanov, P., Gazda, M., Beckham, C., Kautz, J., & Kadoury, S. (2022). Towards annotation-efficient segmentation via image-to-image translation. Medical Image Analysis, 82, 102624 (16 pages). Lien externe
Vorontsov, E., & Kadoury, S. (octobre 2021). Label noise in segmentation networks: Mitigation must deal with bias [Communication écrite]. 1st MICCAI Workshop on Data Augmentation, Labelling and Imperfections (DALI 2021). Lien externe
Vorontsov, E. (2020). On Medical Image Segmentation and on Modeling Long Term Dependencies [Thèse de doctorat, Polytechnique Montréal]. Disponible
Vorontsov, E., Cerny, M., Régnier, P., Di Jorio, L., Pal, C. J., Lapointe, R., Vandenbroucke-Menu, F., Turcotte, S., Kadoury, S., & Tang, A. (2019). Deep learning for automated segmentation of liver lesions at cCT in patients with colorectal cancer liver metastases. Radiology: Artificial Intelligence, 1(2), 180014. Lien externe
Vorontsov, E., Tang, A., Pal, C. J., & Kadoury, S. (avril 2018). Liver lesion segmentation informed by joint liver segmentation [Communication écrite]. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, D.C.. Lien externe
Vorontsov, E., Abi-Jaoudeh, N., & Kadoury, S. (2014). Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models. Dans Abdominal Imaging. Computational and Clinical Applications : 6th International Workshop (ABDI 2014) held in conjunction with (MICCAI 2014) (Vol. 8676, 74-83). Lien externe
