Ghasem Hajianfar
Orcid: 0000-0001-5359-2407
According to our database1,
Ghasem Hajianfar
authored at least 20 papers
between 2019 and 2024.
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Bibliography
2024
Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study.
Medical Biol. Eng. Comput., August, 2024
Differentiation of COVID-19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study.
Int. J. Imaging Syst. Technol., March, 2024
Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images.
CoRR, 2024
2023
Left Ventricular Myocardial Dysfunction Evaluation in Thalassemia Patients Using Echocardiographic Radiomic Features and Machine Learning Algorithms.
J. Digit. Imaging, December, 2023
Myocardial Perfusion SPECT Imaging Radiomic Features and Machine Learning Algorithms for Cardiac Contractile Pattern Recognition.
J. Digit. Imaging, April, 2023
2022
COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images.
Int. J. Imaging Syst. Technol., 2022
COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14, 339 patients.
Comput. Biol. Medicine, 2022
Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images.
Comput. Biol. Medicine, 2022
Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection.
Comput. Biol. Medicine, 2022
Multi-modality fusion coupled with deep learning for improved outcome prediction in head and neck cancer.
Proceedings of the Medical Imaging 2022: Image Processing, 2022
Deep Learning and Machine Learning Techniques for Automated PET/CT Segmentation and Survival Prediction in Head and Neck Cancer.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022
Prediction of TNM stage in head and neck cancer using hybrid machine learning systems and radiomics features.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022
Advanced survival prediction in head and neck cancer using hybrid machine learning systems and radiomics features.
Proceedings of the Medical Imaging 2022: Biomedical Applications in Molecular, 2022
2021
Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients.
Comput. Biol. Medicine, 2021
Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning.
Comput. Biol. Medicine, 2021
Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2021
2019
Non-Invasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Using Computed Tomography Radiomics Features and Machine Learning.
CoRR, 2019
Non-Invasive MGMT Status Prediction in GBM Cancer Using Magnetic Resonance Images (MRI) Radiomics Features: Univariate and Multivariate Machine Learning Radiogenomics Analysis.
CoRR, 2019
Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches.
CoRR, 2019
PET/CT Radiomic Sequencer for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients.
CoRR, 2019