Documents dont l'auteur est "Lemay, Andreanne"

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Ahmed, S. R., Befano, B., Lemay, A., Egemen, D., Rodriguez, A. C., Angara, S., Desai, K., Jeronimo, 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). Lien externe

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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. Lien externe

Gros, C., Lemay, A., & Cohen-Adad, J. (2021). SoftSeg: Advantages of soft versus binary training for image segmentation. Medical Image Analysis, 71, 12 pages. Lien externe

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Lemay, A., Hoebel, K., Bridge, C. P., Befano, B., De Sanjose, S., Egemen, D., Rodriguez, 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. Lien externe

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. Disponible

Lemay, A., Gros, C., Vincent, O., Liu, Y., Cohen, J. P., & Cohen-Adad, J. (juillet 2021). Benefits of linear conditioning for segmentation using metadata [Communication écrite]. 4th Conference on Medical Imaging with Deep Learning (CMDL 2021), Lubeck, Germany. Lien externe

Lemay, A., Gros, C., Vincent, O., Liu, Y., Cohen, J. P., & Cohen-Adad, J. (juillet 2021). Benefits of Linear Conditioning with Metadata for Image Segmentation [Présentation]. Dans 4th Conference on Medical Imaging with Deep Learning (MIDL 2021). Publié dans Proceedings of Machine Learning Research, 143. Lien externe