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Ahmad Chaddad
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2020 – today
- 2024
- [j22]Ahmad Chaddad
, Yihang Wu
Federated Learning for Healthcare Applications. IEEE Internet Things J. 11(5): 7339-7358 (2024) - [c39]Ahmad Chaddad, Yuchen Jiang:
Reinforcement Learning for Medical Image Classification Based on Deep Deterministic Policy Gradients. ISBI 2024: 1-4 - [c38]Yihang Wu, Christian Desrosiers, Ahmad Chaddad:
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification. MICCAI (12) 2024: 531-541 - [i9]Yihang Wu, Christian Desrosiers, Ahmad Chaddad:
FACMIC: Federated Adaptative CLIP Model for Medical Image Classification. CoRR abs/2410.14707 (2024) - 2023
- [j21]Ahmad Chaddad
A texture-based method for predicting molecular markers and survival outcome in lower grade glioma. Appl. Intell. 53(21): 24724-24738 (2023) - [j20]Saima Rathore, Muhammad Aksam Iftikhar, Ahmad Chaddad, Ashish Singh
Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles. Comput. Methods Programs Biomed. 242: 107812 (2023) - [j19]Ahmad Chaddad
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine. IEEE CAA J. Autom. Sinica 10(4): 859-876 (2023) - [j18]Ahmad Chaddad
CNN Approach for Predicting Survival Outcome of Patients With COVID-19. IEEE Internet Things J. 10(15): 13742-13753 (2023) - [j17]Ahmad Chaddad
Survey of Explainable AI Techniques in Healthcare. Sensors 23(2): 634 (2023) - [j16]Ahmad Chaddad
Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques. Sensors 23(14): 6434 (2023) - [c37]Ahmad Chaddad:
Acceleration of Convolutional Neural Networks. BIBE 2023: 87-93 - [c36]Yan Hu, Ahmad Chaddad:
Boosting Classification Tasks with Federated Learning: Concepts, Experiments and Perspectives. BIBE 2023: 147-154 - [c35]Ahmad Chaddad, Yuchen Jiang:
Medical Metaverse: A New Virtual Health Experience. BIBE 2023: 414-421 - [c34]Ahmad Chaddad, Changhong He, Yuchen Jiang:
ChatGPT: An Artificial Intelligence-Based Approach to Enhance Medical Applications. BIBE 2023: 439-446 - [c33]Ahmad Chaddad, Yihang Wu
Enhancing Classification Tasks through Domain Adaptation Strategies. BIBM 2023: 1832-1835 - [c32]Ahmad Chaddad:
Stability in Radiomics Analysis: Advancements and Challenges. HealthCom 2023: 1-5 - [c31]Yan Hu, Ahmad Chaddad:
Potential of Federated Learning in Healthcare. HealthCom 2023: 1-2 - [c30]Ahmad Chaddad:
A Comprehensive Analysis of Lung Sound Signals. HealthCom 2023: 196-197 - [c29]Ahmad Chaddad, Yihang Wu
A Practical Simulation for Domain Adaptation Models. HealthCom 2023: 203-204 - [c28]Ahmad Chaddad, Yihang Wu
Building a Better Metaverse: How Federated Learning is Revolutionizing Virtual Worlds. HealthCom 2023: 205-209 - [c27]Ahmad Chaddad, Yuchen Jiang, Changhong He:
OpenAI ChatGPT: A Potential Medical Application. HealthCom 2023: 210-215 - [c26]Binbin Wen, Ahmad Chaddad:
The Use of Explainable Artificial Intelligence in Medicine. HealthCom 2023: 251-252 - [c25]Ahmad Chaddad, Yue Wang, Junjie Feng:
Radiomics for a Comprehensive Assessment of Glioblastoma Multiforme. HealthCom 2023: 253-258 - [c24]Ahmad Chaddad, Yihang Wu
Domain Adaptation in Machine Learning: A Practical Simulation Study. ICTAI 2023: 754-761 - [c23]Ahmad Chaddad, Yousef Katib, Camel Tanougast:
Advances in MRI-Based Radiomics for Prostate Cancer. ISBI 2023: 1-5 - [c22]Ahmad Chaddad, Yousef Katib, Camel Tanougast:
A One-Dimensional Convolutional Neural Network Model for Predicting the Survival Outcome of Coronavirus Disease 2019. ISBI 2023: 1-4 - 2022
- [j15]Ahmad Chaddad
Deep radiomic signature with immune cell markers predicts the survival of glioma patients. Neurocomputing 469: 366-375 (2022) - [j14]Longzhao Huang, Yujie Li, Xu Wang
Gaze Estimation Approach Using Deep Differential Residual Network. Sensors 22(14): 5462 (2022) - [j13]Ahmad Chaddad
Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-Ray Images. IEEE Trans. Neural Networks Learn. Syst. 33(1): 3-11 (2022) - [c21]Yujie Li
Symmetry Structured Analysis Sparse Coding for Key Frame Extraction. ML4CS (1) 2022: 568-585 - [i8]Ahmad Chaddad
Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients. CoRR abs/2206.01897 (2022) - [i7]Ahmad Chaddad
Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images. CoRR abs/2206.01903 (2022) - [i6]Ahmad Chaddad
Future Artificial Intelligence tools and perspectives in medicine. CoRR abs/2206.03289 (2022) - [i5]Ahmad Chaddad
Deep radiomic signature with immune cell markers predicts the survival of glioma patients. CoRR abs/2206.04349 (2022) - [i4]Longzhao Huang, Yujie Li, Xu Wang, Haoyu Wang, Ahmed Bouridane, Ahmad Chaddad
Gaze Estimation Approach Using Deep Differential Residual Network. CoRR abs/2208.04298 (2022) - [i3]Ahmad Chaddad
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine. CoRR abs/2211.09317 (2022) - 2021
- [j12]Ahmad Chaddad
Modeling Texture in Deep 3D CNN for Survival Analysis. IEEE J. Biomed. Health Informatics 25(7): 2454-2462 (2021) - [c20]Mingli Zhang, Fan Zhang
AutoEncoder for Neuroimage. DEXA (2) 2021: 84-90 - [c19]Ahmad Chaddad
Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients. ISBI 2021: 1067-1071 - 2020
- [j11]Ahmad Chaddad
Deep Radiomic Analysis to Predict Gleason Score in Prostate Cancer. IEEE Access 8: 167767-167778 (2020) - [c18]Mingli Zhang, Xin Zhao, Wenbin Zhang, Ahmad Chaddad
Deep Discriminative Learning for Autism Spectrum Disorder Classification. DEXA (1) 2020: 435-443
2010 – 2019
- 2019
- [j10]Ahmad Chaddad
Predicting the Gene Status and Survival Outcome of Lower Grade Glioma Patients With Multimodal MRI Features. IEEE Access 7: 75976-75984 (2019) - [j9]Ahmad Chaddad
Deep Radiomic Analysis Based on Modeling Information Flow in Convolutional Neural Networks. IEEE Access 7: 97242-97252 (2019) - [j8]Ahmad Chaddad
Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time. IEEE J. Biomed. Health Informatics 23(2): 795-804 (2019) - [c17]Ahmad Chaddad
Deep Radiomic Features from MRI Scans Predict Survival Outcome of Recurrent Glioblastoma. RNO-AI@MICCAI 2019: 36-43 - [c16]Saima Rathore, Ahmad Chaddad
Imaging Signature of 1p/19q Co-deletion Status Derived via Machine Learning in Lower Grade Glioma. RNO-AI@MICCAI 2019: 61-69 - [i2]Ahmad Chaddad, Saima Rathore, Mingli Zhang, Christian Desrosiers, Tamim Niazi:
Deep radiomic features from MRI scans predict survival outcome of recurrent glioblastoma. CoRR abs/1911.06687 (2019) - 2018
- [j7]Ahmad Chaddad
Deep Radiomic Analysis of MRI Related to Alzheimer's Disease. IEEE Access 6: 58213-58221 (2018) - [j6]Ahmad Chaddad
Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Medical Biol. Eng. Comput. 56(12): 2287-2300 (2018) - 2017
- [j5]Ahmad Chaddad
Corrigendum to "Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models". Int. J. Biomed. Imaging 2017: 3247974:1 (2017) - [i1]Ahmad Chaddad, Behnaz Naisiri, Marco Pedersoli, Eric Granger, Christian Desrosiers, Matthew Toews:
Modeling Information Flow Through Deep Neural Networks. CoRR abs/1712.00003 (2017) - 2016
- [j4]Ahmad Chaddad
Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. Brain Informatics 3(1): 53-61 (2016) - [j3]Ahmad Chaddad
Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients. Medical Biol. Eng. Comput. 54(11): 1707-1718 (2016) - [c15]Ahmad Chaddad
Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme. EMBC 2016: 4035-4038 - [c14]Ahmad Chaddad
Multispectral texture analysis of histopathological abnormalities in colorectal tissues. ICIP 2016: 2628-2632 - [c13]Kuldeep Kumar
Spatially constrained sparse regression for the data-driven discovery of Neuroimaging biomarkers. ICPR 2016: 2162-2167 - [c12]Ahmad Chaddad
Local discriminative characterization of MRI for Alzheimer's disease. ISBI 2016: 1-5 - [c11]Ahmad Chaddad
Phenotypic characterization of glioblastoma identified through shape descriptors. Computer-Aided Diagnosis 2016: 97852M - [c10]Ahmad Chaddad
GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes. Image Processing 2016: 978424 - 2015
- [j2]Ahmad Chaddad
High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features. Adv. Bioinformatics 2015: 728164:1-728164:7 (2015) - [j1]Ahmad Chaddad
Automated Feature Extraction in Brain Tumor by Magnetic Resonance Imaging Using Gaussian Mixture Models. Int. J. Biomed. Imaging 2015: 868031:1-868031:11 (2015) - [c9]Hawraa Haj-Hassan, Ahmad Chaddad
Comparison of segmentation techniques for histopathological images. DICTAP 2015: 80-85 - [c8]Ahmad Chaddad
Radiomics texture feature extraction for characterizing GBM phenotypes using GLCM. ISBI 2015: 84-87 - 2014
- [c7]Ahmad Chaddad, Camel Tanougast, Abbas Dandache:
Snake Method Enhanced using Canny Approach Implementation for Cancer Cells Detection in Real Time . BIODEVICES 2014: 187-192 - [c6]Ahmad Chaddad
Brain function evaluation using enhanced fNIRS signals extraction. CISS 2014: 1-4 - [c5]Ahmad Chaddad
Brain tumor identification using Gaussian Mixture Model features and Decision Trees classifier. CISS 2014: 1-4 - [c4]Ahmad Chaddad
Low-noise transimpedance amplifier dedicated to biomedical devices: Near infrared spectroscopy system. CoDIT 2014: 601-604 - [c3]Ahmad Chaddad
Quantitative texture analysis for Glioblastoma phenotypes discrimination. CoDIT 2014: 605-608 - [c2]Pattana Wangaryattawanich, Jixin Wang, Ginu A. Thomas, Ahmad Chaddad
Survival analysis of pre-operative GBM patients by using quantitative image features. CoDIT 2014: 625-627 - [c1]Hawraa Haj-Hassan, Ahmad Chaddad
Segmentation of abnormal cells by using level set model. CoDIT 2014: 770-773
Coauthor Index
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last updated on 2024-11-28 21:27 CET by the dblp team
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