Snake Method Enhanced using Canny Approach Implementation for Cancer Cells Detection in Real Time . - Ahmad Chaddad; Camel Tanougast; Abbas Dandache (BIODEVICES, 2014)

Brain function evaluation using enhanced fNIRS signals extraction. - Ahmad Chaddad (CISS, 2014)

Brain tumor identification using Gaussian Mixture Model features and Decision Trees classifier. - Ahmad Chaddad; Pascal O. Zinn; Rivka R. Colen (CISS, 2014)

Low-noise transimpedance amplifier dedicated to biomedical devices: Near infrared spectroscopy system. - Ahmad Chaddad; Camel Tanougast (CoDIT, 2014)

Quantitative texture analysis for Glioblastoma phenotypes discrimination. - Ahmad Chaddad; Pascal O. Zinn; Rivka R. Colen (CoDIT, 2014)

Segmentation of abnormal cells by using level set model. - Hawraa Haj-Hassan; Ahmad Chaddad; Camel Tanougast; Youssef Harkouss (CoDIT, 2014)

Survival analysis of pre-operative GBM patients by using quantitative image features. - Pattana Wangaryattawanich; Jixin Wang; Ginu A. Thomas; Ahmad Chaddad; Pascal O. Zinn; Rivka R. Colen (CoDIT, 2014)

Comparison of segmentation techniques for histopathological images. - Hawraa Haj-Hassan; Ahmad Chaddad; Camel Tanougast; Youssef Harkouss (DICTAP, 2015)

Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM. - Ahmad Chaddad; Pascal O. Zinn; Rivka R. Colen (ISBI, 2015)

High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. - Ahmad Chaddad; Camel Tanougast (Adv. Bioinformatics, 2015)

Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models. - Ahmad Chaddad (Int. J. Biomed. Imaging, 2015)

Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme. - Ahmad Chaddad; Christian Desrosiers; Matthew Toews (EMBC, 2016)

Multispectral texture analysis of histopathological abnormalities in colorectal tissues. - Ahmad Chaddad; Christian Desrosiers; Lama Hassan; Matthew Toews (ICIP, 2016)

Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers. - Kuldeep Kumar; Christian Desrosiers; Ahmad Chaddad; Matthew Toews (ICPR, 2016)

Local discriminative characterization of MRI for Alzheimer's disease. - Ahmad Chaddad; Christian Desrosiers; Matthew Toews (ISBI, 2016)

Phenotypic characterization of glioblastoma identified through shape descriptors. - Ahmad Chaddad; Christian Desrosiers; Matthew Toews (Computer-Aided Diagnosis, 2016)

GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes. - Ahmad Chaddad; Christian Desrosiers; Matthew Toews (Image Processing, 2016)

Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. - Ahmad Chaddad; Camel Tanougast (Brain Informatics, 2016)

Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients. - Ahmad Chaddad; Camel Tanougast (Medical Biol. Eng. Comput., 2016)

Modeling Information Flow Through Deep Neural Networks. - Ahmad Chaddad; Behnaz Naisiri; Marco Pedersoli; Eric Granger; Christian Desrosiers; Matthew Toews (CoRR, 2017)

Corrigendum to "Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models". - Ahmad Chaddad; Markus Luedi; Pascal O. Zinn; Rivka R. Colen (Int. J. Biomed. Imaging, 2017)

Deep Radiomic Analysis of MRI Related to Alzheimer's Disease. - Ahmad Chaddad; Christian Desrosiers; Tamim Niazi (IEEE Access, 2018)

Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. - Ahmad Chaddad; Siham Sabri; Tamim Niazi; Bassam Abdulkarim (Medical Biol. Eng. Comput., 2018)

Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma. - Ahmad Chaddad; Mingli Zhang; Christian Desrosiers; Tamim Niazi (RNO-AI@MICCAI, 2019)

Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma. - Saima Rathore; Ahmad Chaddad; Nadeem Haider Bukhari; Tamim Niazi (RNO-AI@MICCAI, 2019)

Predicting the Gene Status and Survival Outcome of Lower Grade Glioma Patients With Multimodal MRI Features. - Ahmad Chaddad; Christian Desrosiers; Bassam Abdulkarim; Tamim Niazi (IEEE Access, 2019)

Deep Radiomic Analysis Based on Modeling Information Flow in Convolutional Neural Networks. - Ahmad Chaddad; Matthew Toews; Christian Desrosiers; Tamim Niazi (IEEE Access, 2019)

Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma. - Ahmad Chaddad; Saima Rathore; Mingli Zhang; Christian Desrosiers; Tamim Niazi (CoRR, 2019)

Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time. - Ahmad Chaddad; Paul Daniel; Christian Desrosiers; Matthew Toews; Bassam Abdulkarim (IEEE J. Biomed. Health Informatics, 2019)

Deep Discriminative Learning for Autism Spectrum Disorder Classification. - Mingli Zhang; Xin Zhao; Wenbin Zhang 002; Ahmad Chaddad; Alan C. Evans; Jean-Baptiste Poline (DEXA (1), 2020)

Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer. - Ahmad Chaddad; Michael J. Kucharczyk; Christian Desrosiers; Idowu Paul Okuwobi; Yousef Katib; Mingli Zhang; Saima Rathore; Paul Sargos; Tamim Niazi (IEEE Access, 2020)

AutoEncoder for Neuroimage. - Mingli Zhang; Fan Zhang 045; Jianxin Zhang 001; Ahmad Chaddad; Fenghua Guo; Wenbin Zhang 002; Ji Zhang 001; Alan C. Evans (DEXA (2), 2021)

Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients. - Ahmad Chaddad; Mingli Zhang; Lama Hassan; Tamim Niazi (ISBI, 2021)

Modeling Texture in Deep 3D CNN for Survival Analysis. - Ahmad Chaddad; Paul Sargos; Christian Desrosiers (IEEE J. Biomed. Health Informatics, 2021)

Symmetry Structured Analysis Sparse Coding for Key Frame Extraction. - Yujie Li 002; Benying Tan; Shuxue Ding; Christian Desrosiers; Ahmad Chaddad (ML4CS (1), 2022)

Future Artificial Intelligence tools and perspectives in medicine. - Ahmad Chaddad; Yousef Katib; Lama Hassan (CoRR, 2022)

Deep radiomic signature with immune cell markers predicts the survival of glioma patients. - Ahmad Chaddad; Paul Daniel; Mingli Zhang; Saima Rathore; Paul Sargos; Christian Desrosiers; Tamim Niazi (Neurocomputing, 2022)

Gaze Estimation Approach Using Deep Differential Residual Network. - Longzhao Huang; Yujie Li; Xu Wang 060; Haoyu Wang; Ahmed Bouridane; Ahmad Chaddad (Sensors, 2022)

Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-Ray Images. - Ahmad Chaddad; Lama Hassan; Christian Desrosiers (IEEE Trans. Neural Networks Learn. Syst., 2022)

Acceleration of Convolutional Neural Networks. - Ahmad Chaddad (BIBE, 2023)

ChatGPT: An Artificial Intelligence-Based Approach to Enhance Medical Applications. - Ahmad Chaddad; Changhong He; Yuchen Jiang (BIBE, 2023)

Medical Metaverse: A New Virtual Health Experience. - Ahmad Chaddad; Yuchen Jiang (BIBE, 2023)

Boosting Classification Tasks with Federated Learning: Concepts, Experiments and Perspectives. - Yan Hu; Ahmad Chaddad (BIBE, 2023)

Enhancing Classification Tasks through Domain Adaptation Strategies. - Ahmad Chaddad; Yihang Wu (BIBM, 2023)

Stability in Radiomics Analysis: Advancements and Challenges. - Ahmad Chaddad (HealthCom, 2023)

A Comprehensive Analysis of Lung Sound Signals. - Ahmad Chaddad (HealthCom, 2023)

OpenAI ChatGPT: A Potential Medical Application. - Ahmad Chaddad; Yuchen Jiang; Changhong He (HealthCom, 2023)

A Practical Simulation for Domain Adaptation Models. - Ahmad Chaddad; Yihang Wu (HealthCom, 2023)

Radiomics for a Comprehensive Assessment of Glioblastoma Multiforme. - Ahmad Chaddad; Yue Wang; Junjie Feng (HealthCom, 2023)

Building a Better Metaverse: How Federated Learning is Revolutionizing Virtual Worlds. - Ahmad Chaddad; Yihang Wu; Reem Kateb (HealthCom, 2023)

Potential of Federated Learning in Healthcare. - Yan Hu; Ahmad Chaddad (HealthCom, 2023)

The Use of Explainable Artificial Intelligence in Medicine. - Binbin Wen; Ahmad Chaddad (HealthCom, 2023)

Domain Adaptation in Machine Learning: A Practical Simulation Study. - Ahmad Chaddad; Yihang Wu (ICTAI, 2023)

Advances in MRI-Based Radiomics for Prostate Cancer. - Ahmad Chaddad; Yousef Katib; Camel Tanougast (ISBI, 2023)

A One-Dimensional Convolutional Neural Network Model for Predicting the Survival Outcome of Coronavirus Disease 2019. - Ahmad Chaddad; Yousef Katib; Camel Tanougast (ISBI, 2023)

Survey of Explainable AI Techniques in Healthcare. - Ahmad Chaddad; Jihao Peng; Jian Xu; Ahmed Bouridane (Sensors, 2023)

Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine. - Ahmad Chaddad; Qizong Lu; Jiali Li; Yousef Katib; Reem Kateb; Camel Tanougast; Ahmed Bouridane; Ahmed Abdulkadir (IEEE CAA J. Autom. Sinica, 2023)

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. - Ahmad Chaddad; Yihang Wu; Reem Kateb; Ahmed Bouridane (Sensors, 2023)

CNN Approach for Predicting Survival Outcome of Patients With COVID-19. - Ahmad Chaddad; Camel Tanougast (IEEE Internet Things J., 2023)

A texture-based method for predicting molecular markers and survival outcome in lower grade glioma. - Ahmad Chaddad; Lama Hassan; Yousef Katib (Appl. Intell., 2023)

Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles. - Saima Rathore; Muhammad Aksam Iftikhar; Ahmad Chaddad; Ashish Singh; Zeeshan Gillani; Ahmed Abdulkadir (Comput. Methods Programs Biomed., 2023)

Reinforcement Learning for Enhancing Classifier Performance. - Ahmad Chaddad; Yuchen Jiang; Jianjie Ou (BIBM, 2024)

Reinforcement Learning for Medical Image Classification Based on Deep Deterministic Policy Gradients. - Ahmad Chaddad; Yuchen Jiang (ISBI, 2024)

FACMIC: Federated Adaptative CLIP Model for Medical Image Classification. - Yihang Wu; Christian Desrosiers; Ahmad Chaddad (MICCAI (12), 2024)

Federated Learning for Healthcare Applications. - Ahmad Chaddad; Yihang Wu; Christian Desrosiers (IEEE Internet Things J., 2024)

A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models. - Yuchen Jiang; Xinyuan Zhao; Yihang Wu; Ahmad Chaddad (AAAI, 2025)

SHAP-Integrated Convolutional Diagnostic Networks for Feature-Selective Medical Analysis. - Yan Hu; Ahmad Chaddad (ICASSP, 2025)

Enhancing Large Language Model Fine-Tuning with Sharpness-Aware Minimization Under Split Federated Learning. - Benying Tan; Jie Ren; Yujie Li 002; Shuxue Ding; Ahmad Chaddad (ICIC (9), 2025)

SEMI-Supervised Medical Image Segmentation via Dual Networks. - Yunyao Lu; Yihang Wu; Reem Kateb; Ahmad Chaddad (ISBI, 2025)

Deep Modeling and Optimization of Medical Image Classification. - Yihang Wu; Muhammad Owais; Reem Kateb; Ahmad Chaddad (ISBI, 2025)

EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent. - Ahmad Chaddad; Yuchen Jiang; Tareef S. Daqqaq; Reem Kateb (Comput. Biol. Medicine, 2025)

Domain adaptation techniques for natural and medical image classification. - Ahmad Chaddad; Yihang Wu; Reem Kateb; Christian Desrosiers (Inf. Sci., 2025)

Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification. - Ahmad Chaddad; Yihang Wu; Yuchen Jiang; Ahmed Bouridane; Christian Desrosiers (IEEE Trans. Instrum. Meas., 2025)

Integrating Technologies in the Metaverse for Enhanced Healthcare and Medical Education. - Ahmad Chaddad; Yuchen Jiang (IEEE Trans. Learn. Technol., 2025)

Generalizable and Explainable Deep Learning for Medical Image Computing: An Overview. - Ahmad Chaddad; Yan Hu; Yihang Wu; Binbin Wen; Reem Kateb (CoRR, 2025)

Deep Radiomics for Autism Diagnosis and Age Prediction. - Ahmad Chaddad (IEEE Trans. Hum. Mach. Syst., 2025)

FAA-CLIP: Federated Adversarial Adaptation of CLIP. - Yihang Wu; Ahmad Chaddad; Christian Desrosiers; Tareef S. Daqqaq; Reem Kateb (IEEE Internet Things J., 2025)

Classification based deep learning models for lung cancer and disease using medical images. - Ahmad Chaddad; Jihao Peng; Yihang Wu (CoRR, 2025)