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Collaboration networkThis 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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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.
Ahmed, S. R., Befano, B., Lemay, A., Egemen, D., Rodríguez, A. C., Angara, S., Desai, K., Jerónimo, J., Antani, S., Campos, N., Inturrisi, F., Perkins, R., Kreimer, A., Wentzensen, N., Herrero, R., del Pino, M., Quint, W., de Sanjose, S., Schiffman, M., & Kalpathy-Cramer, J. (2023). Reproducible and clinically translatable deep neural networks for cervical screening. Scientific Reports, 13(1), 21772 (18 pages). External link
Gros, C., Lemay, A., Vincent, O., Rouhier, L., Bourget, M.-H., Bucquet, A., Cohen, P., & Cohen-Adad, J. (2021). Ivadomed : a medical imaging deep learning toolbox. Journal of Open Source Software, 6(58), 5 pages. External link
Gros, C., Lemay, A., & Cohen-Adad, J. (2021). SoftSeg: Advantages of soft versus binary training for image segmentation. Medical Image Analysis, 71, 12 pages. External link
Lemay, A., Gros, C., Enamundram, M. V. N. K., & Cohen-Adad, J. (2023). Label fusion and training methods for reliable representation of inter-rater uncertainty. The Journal of Machine Learning for Biomedical Imaging, 1(January 20), 1-27. External link
Lemay, A. (2022). Impact of Soft Segmentation Training on Medical Image Segmentation and Uncertainty Representation [Master's thesis, Polytechnique Montréal]. Available
Lu, C., Lemay, A., Chang, K., Höbel, K., & Kalpathy-Cramer, J. (2022, February). Fair Conformal Predictors for Applications in Medical Imaging [Paper]. 36th AAAI Conference on Artificial Intelligence (AAAI 2022). External link
Lemay, A., Hoebel, K., Bridge, C. P., Befano, B., De Sanjose, S., Egemen, D., Rodríguez, A. C., Schiffman, M., Campbell, J. P., & Kalpathy-Cramer, J. (2022). Improving the repeatability of deep learning models with Monte Carlo dropout. npj Digital Medicine, 5(1), 11 pages. External link
Lemay, A., Gros, C., Zhuo, Z., Zhang, J., Duan, Y., Cohen-Adad, J., & Liu, Y. (2021). Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning. NeuroImage - Clinical, 31, 9 pages. Available
Lemay, A., Gros, C., Vincent, O., Liu, Y., Cohen, J. P., & Cohen-Adad, J. (2021, July). Benefits of linear conditioning for segmentation using metadata [Paper]. 4th Conference on Medical Imaging with Deep Learning (CMDL 2021), Lübeck, Germany. External link
Lemay, A., Gros, C., Vincent, O., Liu, Y., Cohen, J. P., & Cohen-Adad, J. (2021, July). Benefits of Linear Conditioning with Metadata for Image Segmentation [Presentation]. In 4th Conference on Medical Imaging with Deep Learning (MIDL 2021). Published in Proceedings of Machine Learning Research, 143. External link
Lemay, A., Gros, C., Zhuo, Z., Duan, Y., Zhang, J., Cohen-Adad, J., & Liu, Y. (2020, June). Spinal cord tumor segmentation using multimodal deep learning approach [Paper]. 26th OHBM annual meeting. Unavailable
