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Montagnon, E., Cerny, M., Cadrin-Chênevert, A., Hamilton, V., Derennes, T., Ilinca, A., Vandenbroucke-Menu, F., Turcotte, S., Kadoury, S., & Tang, A. (2020). Deep learning workflow in radiology: a primer. Insights into Imaging, 11(22), 15 pages. Lien externe
Mansour, R., Thibodeau Antonacci, A., Bilodeau, L., Vazquez Romaguera, L., Cerny, M., Huet, C., Gilbert, G., Tang, A., & Kadoury, S. (2020). Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence. Physics in Medicine and Biology, 65(8), 16 pages. Lien externe
Maaref, A., Romero, F. P., Montagnon, E., Cerny, M., Nguyen, B., Vandenbroucke, F., Soucy, G., Turcotte, S., Tang, A., & Kadoury, S. (2020). Predicting the Response to FOLFOX-Based Chemotherapy Regimen from Untreated Liver Metastases on Baseline CT: a Deep Neural Network Approach. Journal of Digital Imaging, 33(4), 937-945. Lien externe
Vorontsov, E., Cerny, M., Régnier, P., Di Jorio, L., Pal, C. J., Lapointe, R., Vandenbroucke-Menu, F., Turcotte, S., Kadoury, S., & Tang, A. (2019). Deep learning for automated segmentation of liver lesions at cCT in patients with colorectal cancer liver metastases. Radiology: Artificial Intelligence, 1(2), 180014. Lien externe
Thibodeau-Antonacci, A., Petitclerc, L., Gilbert, G., Bilodeau, L., Olivie, D., Cerny, M., Castel, H., Turcotte, S., Huet, C., Perreault, P., Soulez, G., Chagnon, M., Kadoury, S., & Tang, A. (2019). Dynamic contrast-enhanced MRI to assess hepatocellular carcinoma response to Transarterial chemoembolization using LI-RADS criteria: A pilot study. Magnetic Resonance Imaging, 62, 78-86. Lien externe
