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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.
Lepetit-Aimon, G., Mameri, L., Duval, R., & Cheriet, F. (2025, April). Enhanced Graph Representation for Reliable Retinal Vessel Analysis: CLSA Population-Based Study [Paper]. 22nd International Symposium on Biomedical Imaging (ISBI 2025), Houston, TX, USA. 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
Lepetit-Aimon, G., Boucher, M.-C., Duval, R., & Cheriet, F. (2023, April). Steered Convolutional Neurons to Better Learn the Classification of Retinal Vessels [Paper]. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2023), Cartagena, Colombia (5 pages). External link
Lepetit-Aimon, G. (2018). Architecture complètement convolutive à champ d'activation large pour la segmentation sémantique de la vasculature rétinienne dans les images de fond d'oeil [Master's thesis, École Polytechnique de Montréal]. Available
Lepetit-Aimon, G., Duval, R., & Cheriet, F. (2018, September). Large Receptive Field Fully Convolutional Network for Semantic Segmentation of Retinal Vasculature in Fundus Images [Paper]. 1st International Workshop on Computational Pathology (COMPAY 2018) and 5th International Workshop on Ophthalmic Medical Image Analysis (OMIA 2018), Granada, Spain. External link