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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
de Boisredon d’Assier, M. A., Portafaix, A., Vorontsov, E., Le, W. T., & Kadoury, S. (2024). Image-level supervision and self-training for transformer-based cross-modality tumor segmentation. Medical Image Analysis, 97, 103287 (16 pages). External link
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). External link
Alefsen, M., Vorontsov, E., & Kadoury, S. (2023, October). M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision [Paper]. 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2023), Vancouver, BC, Canada. External link
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). Available
Cros, S., Bouttier, H., Nguyen-Tân, 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). External link
Le, W. T., Vorontsov, E., Romero, F. P., Seddik, L., Elsharief, M. M., Nguyen-Tân, 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. External link
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). External link
Vorontsov, E., & Kadoury, S. (2021, October). Label noise in segmentation networks: Mitigation must deal with bias [Paper]. 1st MICCAI Workshop on Data Augmentation, Labelling and Imperfections (DALI 2021). External link
Cros, S., Vorontsov, E., & Kadoury, S. (2021, April). Managing Class Imbalance in Multi-Organ CT Segmentation in Head and Neck Cancer Patients [Paper]. 18th IEEE International Symposium on Biomedical Imaging (ISBI 2021), Nice, France. External link
Vorontsov, E. (2020). On Medical Image Segmentation and on Modeling Long Term Dependencies [Ph.D. thesis, Polytechnique Montréal]. Available
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. External link
Kerg, G., Goyette, K., Touzel, M. P., Gidel, G., Vorontsov, E., Bengio, Y., & Lajoie, G. (2019, December). Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics [Paper]. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, B.-C.. External link
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. External link
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. External link
Vorontsov, E., Tang, A., Pal, C. J., & Kadoury, S. (2018, April). Liver lesion segmentation informed by joint liver segmentation [Paper]. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, D.C.. External link
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. External link
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., & Pal, C. J. (2016, October). The Importance of Skip Connections in Biomedical Image Segmentation [Paper]. 2nd International Workshop on Deep Learning in Medical Image Analysis (DLMIA 2016), held in conjunction with MICCAI 2016, Athens, Greece. External link
Vorontsov, E., Abi-Jaoudeh, N., & Kadoury, S. (2014). Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models. In Abdominal Imaging. Computational and Clinical Applications : 6th International Workshop (ABDI 2014) held in conjunction with (MICCAI 2014) (Vol. 8676, pp. 74-83). External link
