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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.
Abdolmanafi, A., Cheriet, F., Duong, L., Ibrahim, R., & Dahdah, N. (2019). An automatic diagnostic system of coronary artery lesions in Kawasaki Disease using IV-OCT imaging. Journal of Biophotonics, 13(1), e201900112 (16 pages). External link
Abdolmanafi, A., Duong, L., Dahdah, N., Adib, I. R., & Cheriet, F. (2018). Characterization of coronary artery pathological formations from OCT imaging using deep learning. Biomedical Optics Express, 9(10), 4936-4960. External link
Abdolmanafi, A., Duong, L., Dahdah, N., & Cheriet, F. (2017). Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography. Biomedical Optics Express, 8(2), 18 pages. External link
Benovoy, M., Jacobs, M., Cheriet, F., Dahdah, N., Arai, A. E., & Hsu, L.-Y. (2017). Robust Universal Nonrigid Motion Correction Framework for First-Pass Cardiac MR Perfusion Imaging. Journal of Magnetic Resonance Imaging, 46(4), 1060-1072. External link
Benovoy, M., Dionne, A., Cheriet, F., Maurice, R. L., & Dahdah, N. (2016). Correlation between automatic angio-based coronary artery stiffness assessment and visual oct pathology scoring. Journal of the American College of Cardiology, 67(13), 1672-1672. External link
Benovoy, M., Cheriet, F., Maurice, R. L., & Dahdah, N. (2015). Abstract 153: Automated Angiographic Assessment Of Coronary Artery Vasomotion In Kawasaki Disease Patients. Circulation, 131(suppl_2). External link
Benovoy, M., Jacobs, M., Cheriet, F., Dahdah, N., Arai, A. E., & Li-Yueh, H. (2015, April). Automatic nonrigid motion correction for quantitative first-pass cardiac MR perfusion imaging [Paper]. 12th IEEE International Symposium on Biomedical Imaging (ISBI 2015), New York, NY. External link
Habert, S., Dahdah, N., & Cheriet, F. (2012, July). A novel method for an automatic 3D reconstruction of coronary arteries from angiographic images [Paper]. 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2012), Montréal, Québec. External link
Périé-Curnier, D., Dahdah, N., Foudis, A., & Curnier, D. (2013). Multi-parametric MRI as an indirect evaluation tool of the mechanical properties of in-vitro cardiac tissues. BMC Cardiovascular Disorders, 13(1). Available
Royer-Rivard, R., Girard, F., Dahdah, N., & Cheriet, F. (2020, July). End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences [Paper]. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2020), Montréal, Québec. External link
