El Adoui, M., Larhmam, M. A., Drisis, S., & Benjelloun, M. (2022). Explainable deep learning approach to predict chemotherapy effect on breast tumor’s MRI. In Ayman S. El-Baz and Jasjit S. Suri, State of the Art in Neural Networks and Their Applications (pp. 147-156). Elsevier.  Peer reviewed |
  | Hachache, R., Yahyaouy, A., Riffi, J., Tairi, H., Abibou, S., El adoui, M., & Benjelloun, M. (21 October 2024). Advancing personalized oncology: a systematic review on the integration of artificial intelligence in monitoring neoadjuvant treatment for breast cancer patients. BMC Cancer, 24 (1), 1300. doi:10.1186/s12885-024-13049-0 Peer Reviewed verified by ORBi |
  | EL ADOUI, M., Drisis, S., & Benjelloun, M. (03 August 2022). New Explainable Deep Cnn Design For Classifying Breast Tumor Response Over Neoadjuvant Chemotherapy. Current Medical Imaging Formerly Current Medical Imaging Reviews, 18. doi:10.2174/1573405618666220803124426 Peer reviewed |
 | El Adoui, M., Drisis, S., & Benjelloun, M. (16 June 2020). Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images. International Journal of Computer Assisted Radiology and Surgery, 1861-6429. Peer Reviewed verified by ORBi |
Braman, N., El Adoui, M., Drisis, S., Vulchi, M., Benjelloun, M., Madabhushi, A., & (en collaboration avec 6 autres personnes ). (24 January 2020). Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study. Nature Communications, 2020 (2020), 2001-2034. Peer reviewed |
 | Stylianos, D., El Adoui, M., Flamen, P., Benjelloun, M., Dewind, R., Paesmans, M., Ignatiadis, M., Bali, M., & Lemort, M. (2019). Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI. Journal of Magnetic Resonance Imaging. Peer Reviewed verified by ORBi |
 | El Adoui, M., Mahmoudi, S., LARHMAM, M. A., & Benjelloun, M. (29 June 2019). MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures. Computers, 8 (3), 52-63. doi:10.3390/computers8030052 Peer Reviewed verified by ORBi |
El Adoui, M., Drisis, S., & Benjelloun, M. (2019). 3D Deep Learning approach to predict breast tumor response to chemotherapy using two DCE-MRI volumes. International Journal of Computer Assisted Radiology and Surgery. Peer Reviewed verified by ORBi |
 | El Adoui, M., Vulchi, M., Braman, N., Turk, P., Etesami, M., Drisis, S., Plecha, D., Benjelloun, M., Madabhushi, A., & Abraham, J. (26 May 2019). Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI. Journal of Clinical Oncology, 37 (15), 593-593. doi:10.1200/JCO.2019.37.15_suppl.593 Peer Reviewed verified by ORBi |
 | Mahmoudi, S., Belarbi, M. A., El Adoui, M., Larhmam, M., & Lecron, F. (2018). Real Time Web-based Toolbox for Computer Vision. Journal of Science and Technology of the Arts. Peer Reviewed verified by ORBi |
El Adoui, M., Drisis, S., & Benjelloun, M. (2018). A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images. International Journal of Computer Assisted Radiology and Surgery. Peer Reviewed verified by ORBi |
 | El Adoui, M., Drisis, S., Larhmam, M., Lemort, M., & Benjelloun, M. (20 September 2017). Breast cancer heterogeneity analysis as index of response to treatment using MRI images: A review. Imaging in Medicine, 9 (4), 109-119. Peer Reviewed verified by ORBi |
Amkrane, Y., El Adoui, M., & Benjelloun, M. (2020). Towards Breast Cancer Response Prediction using Artificial Intelligence and Radiomics [Paper presentation]. The 5th IEEE International Conference on Cloud Computing Technologies and Applications, Marrakech, Morocco. |
El Adoui, M., Braman, N., Vulchi, M., Turk, P., Etesami, M., Plecha, D., Stylianos, D., Benjelloun, M., Abraham, J., & Madabhushi, A. (2019). Validation of neural network approach for the prediction of HER2-targeted neoadjuvant chemotherapy response from pretreatment MRI: A multi-site study [Paper presentation]. SAN ANTONIO BREAST CANCER SYMPOSIUM, San Antonio, United States - Texas. |
 | Benjelloun, M., El Adoui, M., LARHMAM, M. A., & Mahmoudi, S. (2019). Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning [Paper presentation]. The 4th IEEE International Conference on Cloud Computing Technologies and Applications, Brussels, Belgium. |
El Adoui, M., Drisis, S., & Benjelloun, M. (2019). Predict breast tumor response to chemotherapy using a 3D deep learning architecture applied to DCE-MRI data [Paper presentation]. International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain. |
El Adoui, M., LARHMAM, M. A., Drisis, S., & Benjelloun, M. (2019). Deep Learning approach predicting breast tumor response to neoadjuvant treatment using DCE-MRI volumes acquired before and after chemotherapy [Paper presentation]. SPIE MEDICAL IMAGING Computer-Aided Diagnosis, San Diego, United States - California. |
 | Debauche, O., Mahmoudi, S., Belarbi, M. A., El Adoui, M., & Mahmoudi, S. (2018). Internet of Things: learning and practices: Application to Smart Home [Paper presentation]. International Conference on Advanced Communication Technologies and Networking (CommNet'18), Marrakech, Morocco. doi:10.1109/COMMNET.2018.8360247 |
 | El Adoui, M., Drisis, S., & Benjelloun, M. (2017). Analyzing Breast Tumor Heterogeneity To Predict The Response To Chemotherapy Using 3D MR Images Registration [Paper presentation]. International Conference on Smart Digital Environment (ICSDE'17), Rabat, Morocco. |
Mahmoudi, S., El Adoui, M., Belarbi, M. A., Larhmam, M., & Lecron, F. (2017). Cloud-based Platform for Computer Vision Applications [Paper presentation]. International Conference on Smart Digital Environment (ICSDE'17), Rabat, Morocco. |
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