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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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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.
Asgari Taghanaki, S., Abhishek, K., Cohen, J. P., Cohen-Adad, J., & Hamarneh, G. (2020). Deep semantic segmentation of natural and medical images: a review. Artificial Intelligence Review, 54(1), 137-178. External link
Gotlieb, N., Azhie, A., Sharma, D., Spann, A., Suo, N.-J., Tran, J., Orchanian-Cheff, A., Wang, B., Goldenberg, A., Chassé, M., Cardinal, H., Cohen, J. P., Lodi, A., Dieudé, M., & Bhat, M. (2022). The promise of machine learning applications in solid organ transplantation. npj Digital Medicine, 5(1), 13 pages. External link
Lemay, A., Gros, C., Vincent, O., Liu, Y., Cohen, J. P., & Cohen-Adad, J. (2021, July). Benefits of linear conditioning for segmentation using metadata [Paper]. 4th Conference on Medical Imaging with Deep Learning (CMDL 2021), Lübeck, Germany. External link
Lemay, A., Gros, C., Vincent, O., Liu, Y., Cohen, J. P., & Cohen-Adad, J. (2021, July). Benefits of Linear Conditioning with Metadata for Image Segmentation [Presentation]. In 4th Conference on Medical Imaging with Deep Learning (MIDL 2021). Published in Proceedings of Machine Learning Research, 143. External link
Sylvain, T., Luck, M., Cohen, J. P., Cardinal, H., Lodi, A., & Bengio, Y. (2021, March). Exploring the Wasserstein metric for survival analysis [Paper]. AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges and Applications (SPACA 2021), Palo Alto, CA, USA (13 pages). External link
Weiss, M., Chamorro, S., Girgis, R., Luck, M., Kahou, S. E., Cohen, J. P., Nowrouzezahrai, D., Precup, D., Golemo, F., & Pal, C. J. (2019, October). Navigation agents for the visually impaired:a sidewalk simulator and experiments [Paper]. Conference on Robot Learning (CoRL 2019), Osaka, Japan (14 pages). Published in Proceedings of Machine Learning Research, 100. External link
