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Documents dont l'auteur est "Montagnon, Emmanuel"

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Nombre de documents: 11

A

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

C

Cheng, P. M., Montagnon, E., Yamashita, R., Pan, I., Cadrin-Chênevert, A., Romero, F. P., Chartrand, G., Kadoury, S., & Tang, A. (2021). Deep Learning: An Update for Radiologists. RadioGraphics, 41(5), 1427-1445. Lien externe

E

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). Lien externe

Elforaici, M. E. A., Azzi, F., Trudel, D., Nguyen, B., Montagnon, E., Tang, A., Turcotte, S., & Kadoury, S. (mai 2024). Cell-Level GNN-Based Prediction of Tumor Regression Grade in Colorectal Liver Metastases From Histopathology Images [Communication écrite]. 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece (5 pages). Lien externe

M

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). 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

S

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., 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

V

Vianna, P., Kulbay, M., Boustros, P., Calce, S.-I., Larocque-Rigney, C., Patry-Beaudoin, L., Luo, Y. H., Chaudary, M., Kadoury, S., Nguyen, B., Montagnon, E., Belilovsky, E., Wolf, G., Chasse, M., Tang, A., & Cloutier, G. (septembre 2023). Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images [Communication écrite]. IEEE International Ultrasonics Symposium (IUS 2023), Montreal, Qc, Canada (4 pages). Lien externe

Vianna, P., Calce, S.-I., Boustros, P., Larocque-Rigney, C., Patry-Beaudoin, L., Luo, Y. H., Aslan, E., Marinos, J., Alamri, T. M., Vu, K.-N., Murphy-Lavallée, J., Billiard, J.-S., Montagnon, E., Li, H., Kadoury, S., Nguyen, B. N., Gauthier, S., Therien, B., Rish, I., ... Tang, A. (2023). Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis. Radiology, 309(1), e230659 (10 pages). Lien externe

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