Arman Rahmim
Orcid: 0000-0002-9980-2403
According to our database1,
Arman Rahmim
authored at least 63 papers
between 2008 and 2024.
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Bibliography
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
Comput. Methods Programs Biomed., January, 2024
CoRR, 2024
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset.
CoRR, 2024
2023
Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer.
Comput. Methods Programs Biomed., October, 2023
Prediction of drug amount in Parkinson's disease using hybrid machine learning systems and radiomics features.
Int. J. Imaging Syst. Technol., July, 2023
Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss.
Comput. Biol. Medicine, May, 2023
Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images.
CoRR, 2023
Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images.
CoRR, 2023
Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets.
IEEE Access, 2023
Observer study-based evaluation of TGAN architecture used to generate oncological PET images.
Proceedings of the Medical Imaging 2023: Image Perception, 2023
Revisiting the supervision level in semi-supervised learning for automated tumor segmentation: application to lymphoma FDG PET imaging.
Proceedings of the Medical Imaging 2023: Image Processing, 2023
State-of-the-art object detection algorithms for small lesion detection in PSMA PET: use of rotational maximum intensity projection (MIP) images.
Proceedings of the Medical Imaging 2023: Image Processing, 2023
A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset.
Proceedings of the Medical Imaging 2023: Image Processing, 2023
2022
Medical Image Anal., 2022
Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine - The Bethesda Report (AI Summit 2022).
CoRR, 2022
Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features.
CoRR, 2022
Comput. Methods Programs Biomed., 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
Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning.
Comput. Biol. Medicine, 2022
Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics.
Comput. Biol. Medicine, 2022
Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images.
Proceedings of the Medical Imaging 2022: Image Processing, 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
Proceedings of the Medical Imaging 2022: Image Processing, 2022
A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma.
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
A U-Net Convolutional Neural Network with Multiclass Dice Loss for Automated Segmentation of Tumors and Lymph Nodes from Head and Neck Cancer PET/CT Images.
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, 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
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics.
CoRR, 2021
Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson's disease.
Comput. Methods Programs Biomed., 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
Comput. Biol. Medicine, 2021
Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images.
Comput. Biol. Medicine, 2021
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 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
2020
Multi-Level Multi-Modality Fusion Radiomics: Application to PET and CT Imaging for Prognostication of Head and Neck Cancer.
IEEE J. Biomed. Health Informatics, 2020
GAN-Based Bi-Modal Segmentation Using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.
Proceedings of the Head and Neck Tumor Segmentation - First Challenge, 2020
2019
Imager-4D: New Software for Viewing Dynamic PET Scans and Extracting Radiomic Parameters from PET Data.
J. Digit. Imaging, 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
Optimized machine learning methods for prediction of cognitive outcome in Parkinson's disease.
Comput. Biol. Medicine, 2019
2018
An Analysis Scheme for Investigation of Effects of Various Parameters on Signals in Acoustic-Resolution Photoacoustic Microscopy of Mice Brain: a Simulation Study.
CoRR, 2018
Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition.
Comput. Methods Programs Biomed., 2018
Radiomics analysis of baseline F-FDG PET/CT images for improved prognosis in nasopharyngeal carcinoma.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018
2017
Linking dopaminergic reward signals to the development of attentional bias: A positron emission tomographic study.
NeuroImage, 2017
A radiative transfer equation-based image-reconstruction method incorporating boundary conditions for diffuse optical imaging.
Proceedings of the Medical Imaging 2017: Biomedical Applications in Molecular, 2017
2016
Medical Biol. Eng. Comput., 2016
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016
2014
Proceedings of the IEEE 11th International Symposium on Biomedical Imaging, 2014
2013
X-ray CT Metal Artifact Reduction Using Wavelet Domain $L_{0}$ Sparse Regularization.
IEEE Trans. Medical Imaging, September, 2013
2010
Quantification of cerebral cannabinoid receptors subtype 1 (CB1) in healthy subjects and schizophrenia by the novel PET radioligand [<sup>11</sup>C]OMAR.
NeuroImage, 2010
Cannaboid CB1 receptor imaging in vivo in schizophrenia by positron emission tomography.
NeuroImage, 2010
2008
Accurate Event-Driven Motion Compensation in High-Resolution PET Incorporating Scattered and Random Events.
IEEE Trans. Medical Imaging, 2008
Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
Comparative assessment of different energy mapping methods for generation of 511-keV attenuation map from CT images in PET/CT systems: A phantom study.
Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
Analytic system matrix resolution modeling in PET: An application to Rb-82 cardiac imaging.
Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008
Monte Carlo assessment of time-of-flight benefits on the LYSO-based discovery RX PET/CT scanner.
Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008