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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
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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.
Drozdzal, M., Chartrand, G., Vorontsov, E., Shakeri, M., Di Jorio, L., Tang, A., Romero, A., Bengio, Y., Pal, C. J., & Kadoury, S. (2018). Learning normalized inputs for iterative estimation in medical image segmentation. Medical Image Analysis, 44, 1-13. External link
Kohyarnejadfard, I., Aloise, D., Dagenais, M., & Shakeri, M. (2021). A Framework for Detecting System Performance Anomalies Using Tracing Data Analysis. Entropy, 23(8), 1011 (24 pages). External link
Kohyarnejadfard, I., Shakeri, M., & Aloise, D. (2019, April). System performance anomaly detection using tracing data analysis [Paper]. 5th International Conference on Computer and Technology Applications (ICCTA 2019), Istanbul, Turkey. External link
Nemati, H., Azhari, S. V., Shakeri, M., & Dagenais, M. (2021). Host-Based Virtual Machine Workload Characterization Using Hypervisor Trace Mining. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 6(1), 1-25. External link
Shakeri, M., Nahle, I., Finley, E., & Kadoury, S. (2018, April). Inter-vertebral disk modelling from pairs of segmented vertebral models using trainable pre-processing networks [Paper]. 15th IEEE International Symposium on Biomedical Imaging (ISBI 2018), Washington, D.C.. External link
Shakeri, M., Datta, A. N., Malfait, D., Oser, N., Létourneau-Guillon, L., Major, P., Srour, M., Tucholka, A., Kadoury, S., & Lippé, S. (2017). Sub-cortical brain morphometry and its relationship with cognition in rolandic epilepsy. Epilepsy Research, 138(Supplement), 39-45. External link
Shakeri, M. (2016). Analysis of Sub-Cortical Morphology in Benign Epilepsy with Centrotemporal Spikes [Ph.D. thesis, École Polytechnique de Montréal]. Available
Shakeri, M., Lombaert, H., Tripathi, S., & Kadoury, S. (2016, October). Deep spectral-based shape features for Alzheimers disease classification [Paper]. 1st International Workshop on Spectral and Shape Analysis in Medical Imaging (SeSAMI 2016), Athens, Greece. External link
Shakeri, M., Ferrante, E., Tsogkas, S., Lippé, S., Kadoury, S., Kokkinos, I., & Paragios, N. (2016, October). Prior-based coregistration and cosegmentation [Paper]. 19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), Athens, Greece. External link
Shakeri, M., Lombaert, H., Datta, A. N., Oser, N., Létourneau-Guillon, L., Lapointe, L. V., Martin, F., Malfait, D., Tucholka, A., Lippé, S., & Kadoury, S. (2016). Statistical shape analysis of subcortical structures using spectral matching. Computerized Medical Imaging and Graphics, 52, 58-71. External link
Shakeri, M., Lombaert, H., & Kadoury, S. (2015, July). Classification of Alzheimer's Disease Using Discriminant Manifolds of Hippocampus Shapes [Paper]. 1st International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), Lille, France. External link
Shakeri, M., Lombaert, H., Lippé, S., & Kadoury, S. (2014, February). Groupwise shape analysis of the hippocampus using spectral matching [Paper]. SPIE Medical Imaging, San Diego, California, United States. External link
Tripathi, S., Nozadi, S. H., Shakeri, M., & Kadoury, S. (2017, April). Sub-cortical shape morphology and voxel-based features for Alzheimer's disease classification [Paper]. 14th IEEE International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia. External link
