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This graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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A 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.
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
Maaref, A., Romero, F. P., Montagnon, E., Cerny, M., Nguyen, B., Vandenbroucke, F., Soucy, G., Turcotte, S., Tang, A., & Kadoury, S. (2020). Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach. Journal of Digital Imaging, 33(4), 937-945. External link
Romero, F. P., Diler, A., Bisson-Gregoire, G., Turcotte, S., Lapointe, R., Vandenbroucke-Menu, F., Tang, A., & Kadoury, S. (2019, April). End-to-end discriminative deep network for liver lesion classification [Paper]. 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. External link
Romero, F. P., Tang, A., & Kadoury, S. (2019, April). Multi-level batch normalization in deep networks for invasive ductal carcinoma cell discrimination in histopathology images [Paper]. 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. External link