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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.
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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., Bernard, G., Le, W. T., Lalonde, A., Kadoury, S., & Bahig, H. (2023). Artificial intelligence to predict outcomes of head and neck radiotherapy. Clinical and Translational Radiation Oncology, 39, 100590 (9 pages). 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-Tan, 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
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
Elforaici, M. E. A., Montagnon, E., Romero, F. P., Le, W. T., Azzi, F., Trudel, D., Nguyen, B., Turcotte, S., Tang, A., & Kadoury, S. (2025). Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction. Medical Image Analysis, 99, 103346 (16 pages). External link
Grajales, D., Le, W. T., Tran, T., David, S., Dallaire, F., Ember, K., Leblond, F., Menard, C., & Kadoury, S. (2024). Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room. International Journal of Computer Assisted Radiology and Surgery, 9-9. External link
Grajales, D., Kadoury, S., Shams, R., Barkati, M., Delouya, G., Béliveau-Nadeau, D., Nicolas, B., Le, W. T., Benhacene-Boudam, M.-K., Juneau, D., DaSilva, J. N., Carrier, J.-F., Hautvast, G., & Ménard, C. (2022). Performance of an integrated multimodality image guidance and dose-planning system supporting tumor-targeted HDR brachytherapy for prostate cancer. Radiotherapy and Oncology, 166, 154-161. External link
Le, W. T., Bang, C., Cordelle, P., Markel, D., Nguyen-Tan, 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
Le, W. T., Vorontsov, E., Romero, F. P., Seddik, L., Elsharief, M. M., Nguyen-Tan, 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
Le, W. T., Maleki, F., Romero, F. P., Forghani, R., & Kadoury, S. (2020). Overview of Machine Learning: Part 2 Deep Learning for Medical Image Analysis. Neuroimaging Clinics of North America, 30(4), 417-431. External link
Maleki, F., Le, W. T., Sananmuang, T., Kadoury, S., & Forghani, R. (2020). Machine Learning Applications for Head and Neck Imaging. Neuroimaging Clinics of North America, 30(4), 517-529. External link
Touati, R., Le, W. T., & Kadoury, S. (2024). Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis. Physics in Medicine & Biology, 69(15), 155012 (36 pages). Available
Touati, R., Le, W. T., & Kadoury, S. (2021). A feature invariant generative adversarial network for head and neck MRI/CT image synthesis. Physics in Medicine and Biology, 66(9), 18 pages. External link
