Ahmad Chaddad
Ahmad Chaddad
Professor @ School of Artificial Intelligence, GUET; LIVIA-ETS
Verified email at - Homepage
Cited by
Cited by
Radiomics in glioblastoma: current status and challenges facing clinical implementation
A Chaddad, MJ Kucharczyk, P Daniel, S Sabri, BJ Jean-Claude, T Niazi, ...
Frontiers in oncology 9, 374, 2019
Temozolomide induced hypermutation in glioma: evolutionary mechanisms and therapeutic opportunities
P Daniel, S Sabri, A Chaddad, B Meehan, B Jean-Claude, J Rak, ...
Frontiers in Oncology 9, 41, 2019
Survey of explainable AI techniques in healthcare
A Chaddad, J Peng, J Xu, A Bouridane
Sensors 23 (2), 634, 2023
Automated Feature Extraction in brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models
A Chaddad
International Journal of Biomedical Imaging, 11, 2015
Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder
A Chaddad, C Desrosiers, L Hassan, C Tanougast
BMC neuroscience 18, 1-12, 2017
Multimodal radiomic features for the predicting gleason score of prostate cancer
A Chaddad, MJ Kucharczyk, T Niazi
Cancers 10 (8), 249, 2018
Prediction of survival with multi-scale radiomic analysis in glioblastoma patients
A Chaddad, S Sabri, T Niazi, B Abdulkarim
Medical & biological engineering & computing 56, 2287-2300, 2018
Deep radiomic analysis of MRI related to Alzheimer’s disease
A Chaddad, C Desrosiers, T Niazi
IEEE access 6 (1), 58213-58221, 2018
Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images
A Chaddad, C Tanougast
Brain Informatics 3, 53–61, 2016
Novel radiomic features based on joint intensity matrices for predicting glioblastoma patient survival time
A Chaddad, P Daniel, C Desrosiers, M Toews, B Abdulkarim
IEEE journal of biomedical and health informatics 23 (2), 795-804, 2018
Predicting Gleason score of prostate cancer patients using radiomic analysis
A Chaddad, T Niazi, S Probst, F Bladou, M Anidjar, B Bahoric
Frontiers in oncology 8, 630, 2018
Predicting survival time of lung cancer patients using radiomic analysis
A Chaddad, C Desrosiers, M Toews, B Abdulkarim
Oncotarget 8 (61), 104393, 2017
Classifications of multispectral colorectal cancer tissues using convolution neural network
H Haj-Hassan, A Chaddad, Y Harkouss, C Desrosiers, M Toews, ...
Journal of pathology informatics 8 (1), 1, 2017
Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients
A Chaddad, C Tanougast
Medical & biological engineering & computing 54, 1707-1718, 2016
Glioma grading via analysis of digital pathology images using machine learning
S Rathore, T Niazi, MA Iftikhar, A Chaddad
Cancers 12 (3), 578, 2020
Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age
A Chaddad, C Desrosiers, M Toews
Scientific reports 7 (1), 45639, 2017
Deep CNN models for predicting COVID-19 in CT and x-ray images
A Chaddad, L Hassan, C Desrosiers
Journal of medical imaging 8 (S1), 014502-014502, 2021
Radiomics evaluation of histological heterogeneity using multiscale textures derived from 3D wavelet transformation of multispectral images
A Chaddad, P Daniel, T Niazi
Frontiers in oncology 8, 96, 2018
Multi texture analysis of colorectal cancer continuum using multispectral imagery
A Chaddad, C Desrosiers, A Bouridane, M Toews, L Hassan, ...
PloS one 11 (2), e0149893, 2016
Integration of radiomic and multi-omic analyses predicts survival of newly diagnosed IDH1 wild-type glioblastoma
A Chaddad, P Daniel, S Sabri, C Desrosiers, B Abdulkarim
Cancers 11 (8), 1148, 2019
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