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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.
Daoust, F., Dallaire, F., Tavera, H., Ember, K., Guiot, M.-C., Petrecca, K., & Leblond, F. (2025). Preliminary study demonstrating cancer cells detection at the margins of whole glioblastoma specimens with Raman spectroscopy imaging. Scientific Reports, 15, 6453 (15 pages). Available
David, S., Tavera, H., Tran, T., Dallaire, F., Daoust, F., Tremblay, F., Richer, L., Meterissian, S., & Leblond, F. (2024). Macroscopic inelastic scattering imaging using a hyperspectral line-scanning system identifies invasive breast cancer in lumpectomy and mastectomy specimens. Journal of Biomedical Optics, 29(6), 065004 (16 pages). Available
Daoust, F., Tavera, H., Dallaire, F., Orsini, P., Savard, K., Bismuth, J., McKoy, P., Veilleux, I., Petrecca, K., & Leblond, F. (2023). A clinical Raman spectroscopy imaging system and safety requirements for in situ intraoperative tissue characterization. Analyst, 148(9), 1991-2001. External link
Leblond, F., Dallaire, F., Ember, K., Le Moël, A., Blanquez-Yeste, V., Tavera, H., Sheehy, G., Trang, T., Guiot, M.-C., Weil, A. G., Dudley, R., Hadjipanayis, C. G., & Petrecca, K. (2025). Quantitative assessment of the generalizability of a brain tumor Raman spectroscopy machine learning model to various tumor types including astrocytoma and oligodendroglioma [Discussion or Letter]. Journal of Biomedical Optics, 30(1), 010501 (7 pages). Available
Orsini, P., Desroches, J., Daoust, F., Tavera, H., Dallaire, F., Savard, K., Bismuth, J., McKoy, P., Veilleux, I., Petrecca, K., & Leblond, F. (2023, January). Raman macroscopic imaging system for intraoperative brain cancer detection [Paper]. SPIE BiOS 2023, San Francisco, California. External link
Tavera, H., Sheehy, G., Orsini, P., Bismuth, J., de Denus-Baillargeon, M.-M., Massabki, M., Masson, J.-F., & Leblond, F. (2025). Experimental method to assess depth sensing limits of inelastic scattering measurements using spatial-offset Raman spectroscopy imaging. Journal of Biomedical Optics, 30(S3), S34108 (15 pages). External link
Tavera, H. (2025). Développement d'une méthode d'imagerie utilisant la spectroscopie Raman spatialement décalée afin de détecter la signature moléculaire de tissus biologiques [Master's thesis, Polytechnique Montréal]. Available
