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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.
Bang, C., Gautier, H., Le, W., Lalonde, A., Bernard, G., Markel, D., Nguyen-Tan, F., Filion, É., O’Sullivan, B., Ayad, T., Christopoulos, A., Bissada, É., Guertin, L., Soulières, D., Létourneau-Guillon, L., Kadoury, S., & Bahig, H. (2025). Dynamic prediction of Radiotherapy toxicities in Head and neck cancer using clinical and imaging data. Radiotherapy and Oncology, 111312-111312. External link
Bang, C., Le, W. T., Nguyen-Tan, P. F., Soulières, D., O'Sullivan, B., Christopoulos, A., Bissada, É., Ayad, T., Guertin, L., Lalonde, A., Markel, D., Kadoury, S., & Bahig, H. (2023, October). Dynamic Prediction of Toxicities in Head and Neck Cancer Radiotherapy by 3D Convolutional Neural Network Using Daily Cone-Beam CTs [Abstract]. 65th Annual Meeting of the American Society for Radiation Oncology (ASTRO 2023), San Diego, CA, USA (1 page). Published in International Journal of Radiation Oncology Biology Physics, 117(2). External link
Bang, C., Le, W. T., Nguyen-Tân, P. F., Filion, É., Soulières, D., O'Sullivan, B., Christpoulos, A., Bissada, É., Ayad, T., Guertin, L., Lalonde, A., Markel, D., Kadoury, S., & Bahig, H. (2023, September). Dynamic prediction of toxicities in head and neck cancer radiotherapy by 3D convolutional neural network using daily cone-beam CTS [Abstract]. CARO-COMP Joint Scientific Meeting (CARO-COMP 2023), Montréal, Québec, Canada (1 page). Published in Radiotherapy and Oncology, 186(Suppl.1). External link
Henique, G., Seok Bang, C., Le, W., Nguyen, F., Filion, É., Soulières, D., O'Sullivan, B., Christopoulos, A., Bissada, É., Ayad, T., Guertin, L., Lalonde, A., Markel, D., Kadoury, S., & Bahig, H. (2025). Head and Neck Cancer Radiotherapy Toxicity Prediction across Intra-Fractional Cone-Beam CTs Using Vision Transformers Networks. International Journal of Radiation Oncology*Biology*Physics, 123(1), e343-e343. Presented at 67th Annual Meeting of the American Society for Radiation Oncology (ASTRO 2025), San Francisco, CA, USA. External link
Henique, G., Bang, C., Markel, D., Le, W., Filion, E., Ngyuen-Tan, P. F., Bahig, H., & Kadoury, S. (2024, May). Dose aware toxicity prediction in head and neck cancer patients using a deformable 3D CNN on Daily CBCT Acquisitions [Paper]. IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece (5 pages). External link
Le, W. T., Bang, C., Cordelle, P., Markel, D., Nguyen-Tân, P. F., Bahig, H., & Kadoury, S. (2023, April). Prediction of Head and Neck Radiotherapy Toxicity Using a Deformable 3D CNN on Longitudinal Daily CBCT Acquisitions [Paper]. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2023), Cartagena, Colombia (5 pages). External link
