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Amine Elforaici, M. E., Montagnon, E., Azzi, F., Trudel, D., Nguyen, B., Turcotte, S., Tang, A., & Kadoury, S. (2022, March). Semi-Supervised Tumor Response Grade Classification from Histology Images of Colorectal Liver Metastases [Paper]. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI 2022), Kolkata, India (5 pages). External link
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. External link
Elforaici, M. E. A., Montagnon, E., Romero, F. P., Le, W. T., Azzi, F., Trudel, D., Nguyen, B., Turcotte, S., Tang, A., & Kadoury, S. (2025). Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction. Medical Image Analysis, 99, 103346 (16 pages). External link
Elforaici, M. E. A., Azzi, F., Trudel, D., Nguyen, B., Montagnon, E., Tang, A., Turcotte, S., & Kadoury, S. (2024, May). Cell-Level GNN-Based Prediction of Tumor Regression Grade in Colorectal Liver Metastases From Histopathology Images [Paper]. 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece (5 pages). External link
Montagnon, E., Cerny, M., Hamilton, V., Derennes, T., Ilinca, A., Elforaici, M. E. A., Jabbour, G., Rafie, E., Wu, A., Perdigon Romero, F., Cadrin-Chênevert, A., Kadoury, S., Turcotte, S., & Tang, A. (2024). Radiomics analysis of baseline computed tomography to predict oncological outcomes in patients treated for resectable colorectal cancer liver metastasis. PLOS ONE, 19(9), 0307815 (17 pages). External link
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. External link
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. External link
Romero, F. P., Diler, A., Bisson-Gregoire, G., Turcotte, S., Lapointe, R., Vandenbroucke-Menu, F., Tang, A., & Kadoury, S. (2019, April). End-to-end discriminative deep network for liver lesion classification [Paper]. 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. External link
Saber, R., Tonneau, M., Bahig, S., Malo, J., Belkaid, W., Messaoudene, M., Bouchard, N., Coulombe, F., Joubert, P., Bahig, H., Turcotte, S., Routy, B., & Kadoury, S. (2024, May). Feature Tokenizer-Transformers with Self-Training for The Prediction of PD-L1 Expression of Non-Small Cell Lung Cancer from CT [Paper]. 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece (5 pages). External link
Saber, R., Henault, D., Rebolledo, R., Turcotte, S., & Kadoury, S. (2023, April). Ensemble Tabnet Predicting a T-Cell/MHC-I-Based Immune Profile Biomarker for Colorectal Liver Metastases from CT Images [Paper]. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2023), Cartagena, Colombia (5 pages). External link
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. Available
Saber, R., Routy, B., Turcotte, S., & Kadoury, S. (2023, October). RNA sequencing-based histological subtyping of non-small cell lung cancer with generative adversarial data imputation [Paper]. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2023), Pittsburgh, PA, USA (4 pages). External link
Saber, R., Henault, D., Vorontsov, E., Montagnon, E., Tang, A., Turcotte, S., & Kadoury, S. (2022, February). Prediction of CD3 T-cell infiltration status in colorectal liver metastases: a radiomics-based imaging biomarker [Paper]. Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, CA, USA (7 pages). External link
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. External link
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. External link
