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* These authors have contributed equally to this work. |
 | 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 |
Amkrane, Y., El Adoui, M., & Benjelloun, M. (2020). Towards Breast Cancer Response Prediction using Artificial Intelligence and Radiomics. Paper presented at The 5th IEEE International Conference on Cloud Computing Technologies and Applications, Marrakech, Morocco. |
| 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 |
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 presented at SAN ANTONIO BREAST CANCER SYMPOSIUM, San Antonio, United States - Texas. |
Amkrane, Y., El Adoui, M., & Benjelloun, M. (22 November 2019). Analysing Breast cancer reaction to chemotherapy using Radiomics. Paper presented at Grascomp Doctoral Day (GDD'19), UNamur, Belgium. |
| 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 |
| Benjelloun, M., El Adoui, M., LARHMAM, M. A., & Mahmoudi, S. (2019). Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning. Paper presented at 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 presented at International Work-Conference on Bioinformatics and Biomedical Engineering, Granada, Spain. |
| Amkrane, Y., El Adoui, M., & Benjelloun, M. (18 March 2019). Approches d'imagerie et machine learning pour la prédiction de la réponse d'un cancer du sein à la chimiothérapie. Poster session presented at Workshop UMHAP Medical Research Center, Mons, Belgium. |
| Amkrane, Y., El Adoui, M., & Benjelloun, M. (05 March 2019). Breast cancer response to chemotherapy by Radiomics. Poster session presented at Mardi des Chercheurs 2019 (MdC2019), Mons, Belgium. |
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 presented at SPIE MEDICAL IMAGING Computer-Aided Diagnosis, San Diego, United States - California. |
| El Adoui, M., & Benjelloun, M. (02 July 2018). A Deep Learning approach to predict breast cancer response to chemotherapy using DCE-MRI images. Paper presented at VISion Understanding and Machine intelligence - VISUM 2018, Porto, Portugal. |
| 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 |
| Debauche, O., Mahmoudi, S., Belarbi, M. A., El Adoui, M., & Mahmoudi, S. (2018). Internet of Things: learning and practices: Application to Smart Home. Paper presented at 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. (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., & Benjelloun, M. (27 March 2018). The Prediction Of Breast Cancer Response To Chemotherapy By Deep Learning. Poster session presented at Mardi des chercheurs MDC'18, Wallers Arenberg, France. |
Larhmam, M., El Adoui, M., & Benjelloun, M. (26 March 2018). Evaluating Deep Features for Breast Cancer Response Prediction in MRI Based on GPU Learning. Paper presented at AI & Deep Learning Conference, GTC 2018, NVIDIA, San Jose, United States - California. |
| 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 |
| Mahmoudi, S., Debauche, O., El Adoui, M., & Belarbi, M. A. (2017). L'Internet des Objets met la lumière dans tous ses états. Polytech News (2017). |
| El Adoui, M., Drisis, S., & Benjelloun, M. (2017). Analyzing Breast Tumor Heterogeneity To Predict The Response To Chemotherapy Using 3D MR Images Registration. Paper presented at 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 presented at International Conference on Smart Digital Environment (ICSDE'17), Rabat, Morocco. |
| El Adoui, M., & Benjelloun, M. (07 March 2017). The prediction of breast tumor response to chemotherapy by 3D-MRI images analysis and interpretation. Poster session presented at Huitième édition montoise du MdC : le Mardi des Chercheurs 2017, UMons, Belgium. |
| El Adoui, M., Stylianos, D., & Benjelloun, M. (09 September 2016). Intra tumor heterogeneity as biomarker of response to treatment : prospective evaluation of parametric response mapping applied in MRI volumetric images of breast cancer. Paper presented at EDT-Cancérologie Expérimentale: Annual Mini symposium 'Why and how to integrate the tumor microenvironment and the immune system in therapeutic strategies targeting cancer cells', Bruxelles, Belgium. |
| El Adoui, M., Drisis, S., & Benjelloun, M. (09 September 2016). Prospective evaluation by Parametric Response Mapping (PRM) applied in MRI volumetric images of breast cancer. Poster session presented at EDT-Cancérologie Expérimentale: Annual Mini symposium 'Why and how to integrate the tumor microenvironment and the immune system in therapeutic strategies targeting cancer cells', Bruxelles, Belgium. |
