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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
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
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
