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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.
Guerroumi, N., Playout, C., Laporte, C., & Cheriet, F. (2019, April). Automatic segmentation of the scoliotic spine from mr images [Paper]. 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. External link
Legault, Z., Playout, C., Girard, F., & Cheriet, F. (2025, February). Graph-based representation of retinal lesions for an interpretable diagnosis of diabetic retinopathy [Paper]. Computer-Aided Diagnosis, San Diego, United States (10 pages). External link
Lepetit-Aimon, G., Playout, C., Boucher, M. C., Duval, R., Brent, M. H., & Cheriet, F. (2024). MAPLES-DR: MESSIDOR Anatomical and Pathological Labels for Explainable Screening of Diabetic Retinopathy. Scientific Data, 11(1). External link
Playout, C., Legault, Z., Duval, R., Boucher, M. C., & Cheriet, F. (2024, October). A Region-Based Approach to Diabetic Retinopathy Classification with Superpixel Tokenization [Paper]. Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), Marrakesh, Morocco. External link
Playout, C. (2023). Modélisation interprétable du diagnostic de pathologies rétiniennes par apprentissage profond [Ph.D. thesis, Polytechnique Montréal]. Available
Playout, C., Duval, R., Boucher, M.-C., & Cheriet, F. (2022). Focused Attention in Transformers for interpretable classification of retinal images. Medical Image Analysis, 82, 102608 (13 pages). External link
Playout, C. (2018). Système d'apprentissage multitâche dédié à la segmentation des lésions sombres et claires de la rétine dans les images de fond d'oeil [Master's thesis, École Polytechnique de Montréal]. Available
Playout, C., Duval, R., & Cheriet, F. (2018, September). A multitask learning architecture for simultaneous segmentation of bright and red lesions in fundus images [Paper]. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Granada, Spain. External link