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Items where Author is "Turcotte, Simon"

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Number of items: 11.

A

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

C

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

M

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

R

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

S

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

T

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

V

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

List generated on: Tue Feb 27 07:38:26 2024 EST