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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.
Ember, K. J. I., Ksantini, N., Dallaire, F., Sheehy, G., Tran, T., Dehaes, M., Durand, M., Trudel, D., & Leblond, F. (2024). Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time. The Analyst, 149(22), 5535-5545. Available
Leblond, F., David, S., Pahlavani, S., Ksantini, N., Dallaire, F., Sheehy, G., Hadjapanayis, C., Petrecca, K., & Wilson, B. C. (2024, January). Towards non-contact and real-time large field-of-view single-band Raman spectroscopy imaging in brain cancer surgery [Presentation]. In SPIE Photonics West 2024, San Francisco, Ca, United States. External link
Ember, K., Ksantini, N., Dallaire, F., Sheehy, G., Noé, A., Tran, T., Durand, M., Trudel, D., & Leblond, F. (2024, January). Rapid detection of COVID-19 in liquid saliva using reagent-free Raman spectroscopy [Presentation]. In SPIE Photonics West 2024, San Francisco, California, United States. External link
Zamani, E., Ksantini, N., Sheehy, G., Ember, K. J. I., Baloukas, B., Zabeida, O., Trang, T., Mahfoud, M., Sapieha, J.-E., Martinu, L., & Leblond, F. (2024). Spectral effects and enhancement quantification in healthy human saliva with surface-enhanced Raman spectroscopy using silver nanopillar substrates. Lasers in Surgery and Medicine, 56(2), 206-217. Available
David, S., Ksantini, N., Dallaire, F., Ember, K., Daoust, F., Sheehy, G., Hadjipanayis, C. G., Petrecca, K., Wilson, B. C., & Leblond, F. (2024). Toward noncontact macroscopic imaging of multiple cancers using multi-spectral inelastic scattering detection. Journal of Biophotonics, 2024000087 (7 pages). External link
Ember, K., Daoust, F., Mahfoud, M., Dallaire, F., Ahmad, E. Z., Tran, T., Plante, A., Diop, M.-K., Nguyen, T., St-Georges-Robillard, A., Ksantini, N., Lanthier, J., Filiatrault, A., Sheehy, G., Beaudoin, G., Quach, C., Trudel, D., & Leblond, F. (2022). Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning. Journal of Biomedical Optics, 27(2), 025002 (24 pages). Available
Maubois, B., Ksantini, N., & Leblond, F. (2022, April). Development of a time-domain tissue imaging Raman spectroscopy system increasing inelastic scattering signal-to-background ratio from early-photon time gating [Paper]. Clinical and Translational Biophotonics 2022, Fort Lauderdale, FL, USA. External link
Ksantini, N., Veilleux, I., De Denus-Baillargeon, M.-M., Orsini, P., Dicaire, I., Lecourt, J.-B., Gognau, A., Hernandez, Y., Baylon, A., Massabki, M., Lesage, F., & Leblond, F. (2022). Time-gated interferometric detection increases Raman scattering to fluorescence signal ratio in biological samples. Journal of Biophotonics, 15(1), 6 pages. External link
Ksantini, N. (2021). Spectroscopie Raman de tissus biologiques dans le domaine temporel [Master's thesis, Polytechnique Montréal]. Available
