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Amine Elforaici, M. E., Montagnon, E., Azzi, F., Trudel, D., Nguyen, B., Turcotte, S., Tang, A., & Kadoury, S. (mars 2022). Semi-Supervised Tumor Response Grade Classification from Histology Images of Colorectal Liver Metastases [Communication écrite]. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI 2022), Kolkata, India (5 pages). Lien externe
Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., Kadoury, S., & Tang, A. (2017). Deep Learning: A Primer for Radiologists. RadioGraphics, 37(7), 2113-2131. Lien externe
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
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
Romero, F. P., Diler, A., Bisson-Gregoire, G., Turcotte, S., Lapointe, R., Vandenbroucke-Menu, F., Tang, A., & Kadoury, S. (avril 2019). End-to-end discriminative deep network for liver lesion classification [Communication écrite]. 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. Lien externe
Saber, R., Henault, D., Rebolledo, R., Turcotte, S., & Kadoury, S. (avril 2023). Ensemble Tabnet Predicting a T-Cell/MHC-I-Based Immune Profile Biomarker for Colorectal Liver Metastases from CT Images [Communication écrite]. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2023), Cartagena, Colombia (5 pages). Lien externe
Saber, R., Henault, D., Messaoudi, N., Rebolledo, R., Montagnon, E., Soucy, G., Stagg, J., Tang, A., Turcotte, S., & Kadoury, S. (2023). Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases. Journal of Translational Medicine, 21(1), 16 pages. Disponible
Saber, R., Routy, B., Turcotte, S., & Kadoury, S. (octobre 2023). RNA sequencing-based histological subtyping of non-small cell lung cancer with generative adversarial data imputation [Communication écrite]. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2023), Pittsburgh, PA, USA (4 pages). Lien externe
Saber, R., Henault, D., Vorontsov, E., Montagnon, E., Tang, A., Turcotte, S., & Kadoury, S. (février 2022). Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker [Communication écrite]. Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, CA, USA (7 pages). 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
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
