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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.
Anctil-Robitaille, B., Théberge, A., Jodoin, P.-M., Descoteaux, M., Desrosiers, C., & Lombaert, H. (2022). Manifold-aware synthesis of high-resolution diffusion from structural imaging. Frontiers in Neuroimaging, 1, 20 pages. External link
Anctil-Robitaille, B., Desrosiers, C., & Lombaert, H. (2020, October). Manifold-Aware CycleGAN for High-Resolution Structural-to-DTI Synthesis [Paper]. Computational Diffusion MRI 2020 (CDMRI 2020), 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), Lima, Peru. External link
Delisle, P.-L., Anctil-Robitaille, B., Desrosiers, C., & Lombaert, H. (2021). Realistic image normalization for multi-Domain segmentation. Medical Image Analysis, 74, 102191 (14 pages). External link
Delisle, P.-L., Anctil-Robitaille, B., Desrosiers, C., & Lombaert, H. (2020, April). Adversarial Normalization for Multi Domain Image Segmentation [Paper]. 17th IEEE International Symposium on Biomedical Imaging (ISBI 2020), Iowa City, IA, USA. External link
Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., & Ben Ayed, I. (2019). HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation. IEEE Transactions on Medical Imaging, 38(5), 1116-1126. External link
Desrosiers, C., Galinier, P., Hansen, P., & Hertz, A. (2014). Automated generation of conjectures on forbidden subgraph characterization. Discrete Applied Mathematics, 162, 177-194. External link
Desrosiers, C., Galinier, P., Hertz, A., & Hansen, P. (2010). Improving constrained pattern mining with first-fail-based heuristics. Data Mining and Knowledge Discovery, 23(1), 1-28. External link
Desrosiers, C., Galinier, P., Hertz, A., & Paroz, S. (2009). Using heuristics to find minimal unsatisfiable subformulas in satisfiability problems. Journal of Combinatorial Optimization, 18(2), 124-150. External link
Desrosiers, C. (2008). Techniques pour l'exploration de données structurées et pour la découverte de connaissances en théorie des graphes [Ph.D. thesis, École Polytechnique de Montréal]. Available
Desrosiers, C. (2004). Détection d'ensembles irréductibles incohérents dans des problèmes de satisfaction de contraintes irréalisables [Master's thesis, École Polytechnique de Montréal]. Available
Gaillochet, M., Desrosiers, C., & Lombaert, H. (2024, October). Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision [Paper]. International Workshop on Foundation Models for General Medical AI (MedAGI 2024), Marrakesh, Morocco. Published in Lecture notes in computer science. External link
Gaillochet, M., Desrosiers, C., & Lombaert, H. (2023). Active learning for medical image segmentation with stochastic batches. Medical Image Analysis, 90, 102958 (11 pages). External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2023). Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation. Medical Image Analysis, 90, 102974 (9 pages). External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2022). Learnable Pooling in Graph Convolutional Networks for Brain Surface Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2), 864-876. External link
Gaillochet, M., Desrosiers, C., & Lombaert, H. (2022, September). TAAL: Test-Time Augmentation for Active Learning in Medical Image Segmentation [Paper]. Second MICCAI Workshop on Data Augmentation, Labelling, and Imperfections (DALI 2022, MICCAI 2022), Singapore. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2021, September). SegRecon: Learning Joint Brain Surface Reconstruction and Segmentation from Images [Paper]. 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021), Starsbourg, France. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2020, October). Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation [Paper]. Second International Workshop, UNSURE 2020, and Third International Workshop GRAIL 2020, held in Conjunction with MICCAI 2020, Lima, Peru. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2019, June). Adaptive Graph Convolution Pooling for Brain Surface Analysis [Paper]. 26th Biennal International Conference on Information Processing in Medical Imaging (IPMI 2019), Hong Kong, China. External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2019, July). Cortical parcellation via spectral graph convolutions [Abstract]. Medical Imaging with Deep Learning (MIDL 2019), London, UK (5 pages). External link
Gopinath, K., Desrosiers, C., & Lombaert, H. (2019). Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation. Medical Image Analysis, 54, 297-305. External link
He, R., Gopinath, K., Desrosiers, C., & Lombaert, H. (2020, April). Spectral Graph Transformer Networks for Brain Surface Parcellation [Paper]. 17th IEEE International Symposium on Biomedical Imaging (ISBI 2020), Iowa City, IA, USA. External link
Mei, J., Tremblay, C., Stikov, N., Desrosiers, C., & Frasnelli, J. (2021, February). Differentiation of Parkinson's disease and non-Parkinsonian olfactory dysfunction with structural MRI data [Paper]. Medical Imaging 2021: Computer-Aided Diagnosis (8 pages). External link