| Ahmad Chaddad | 阿哈迈德·恰达德 Professor Associate member, The Laboratory of Imaging, Vision and Artificial Intelligence (LIVIA) Ecole de Technologie Superieure, Montreal, Canada Personal website : http://sai.inpsmart.com:8060/contents/usercenter_30/221.html |
Experiences
- Ph.D. (University of Lorraine, France).
- 7 years of research and teaching experience in Canada (ETS and McGill University) and USA (University of Texas MD Anderson Cancer Center and Villanova University)
-Adjunct professor at the ETS, University of Quebec, Montreal, Canada.
-Project director, The Lady Davis Institute for Medical Research, McGill University, Montreal, Canada
Research Interests
My current research focuses on the development and application of AI techniques to solve problems in the fields of:
-Radiomics and radiogenomics
-Medical image analysis
-Machine learning-deep Learning
Five Selected Publications
[1] Chaddad A., Tanougast C., 2023 “CNN approach for predicting survival outcome of patients with COVID-19”, IEEE Internet of Things, DOI: 10.1109/JIOT.2023.3262882 (March 2023).
[2] Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., ... & Abdulkadir, A. (2023). Explainable, domain-adaptive, and federated artificial intelligence in medicine. IEEE/CAA Journal of Automatica Sinica, 10(4), 859-876.
[3] Chaddad, A., Hassan, L., & Desrosiers, C. (2021). Deep radiomic analysis for predicting coronavirus disease 2019 in computerized tomography and x-ray images. IEEE Transactions on Neural Networks and Learning Systems, 33(1), 3-11.
[4] Chaddad, A., Sargos, P., & Desrosiers, C. (2020). Modeling texture in deep 3D CNN for survival analysis. IEEE Journal of Biomedical and Health Informatics, 25(7), 2454-2462.
[5] Chaddad, A., Daniel, P., Desrosiers, C., Toews, M., & Abdulkarim, B. (2018). Novel radiomic features based on joint intensity matrices for predicting glioblastoma patient survival time. IEEE journal of biomedical and health informatics, 23(2), 795-804.
