167 citations · 457 across the 7 of their papers we have counts for
7 papers
Machine Learning Evaluation Metric Discrepancies across Programming Languages and Their Components: Need for Standardization
Mohammad R. Salmanpour, Morteza Alizadeh, Ghazal Mousavi +5
This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation.…
Non-Invasive Fuhrman Grading of Clear Cell Renal Cell Carcinoma Using Computed Tomography Radiomics Features and Machine Learning
Mostafa Nazari, Isaac Shiri, Ghasem Hajianfar +4
Purpose: To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cells renal cell carcinoma (ccRCC) grade. Methods…
Cardiac SPECT Radiomics Features Repeatability and Reproducibility: A Multi Scanner Phantom Study
Mohammad Edalat-Javid, Isaac Shiri, Ghasem Hajianfar +7
Background: The aim of this study was to assess the robustness of cardiac SPECT radiomics features against changes in imaging settings including acquisition and reconstruction sett…
Non-Invasive MGMT Status Prediction in GBM Cancer Using Magnetic Resonance Images (MRI) Radiomics Features: Univariate and Multivariate Machine Learning Radiogenomics Analysis
Ghasem Hajianfar, Isaac Shiri, Hassan Maleki +4
Background and aim: This study aimed to predict methylation status of the O-6 methyl guanine-DNA methyl transferase (MGMT) gene promoter status by using MRI radiomics features, as…
Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches
Isaac Shiri, Hassan Maleki, Ghasem Hajianfar +5
Aim: In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on PET an…
PET/CT Radiomic Sequencer for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients
Isaac Shiri, Hassan Maleki, Ghasem Hajianfar +5
The aim of this study was to develop radiomic models using PET/CT radiomic features with different machine learning approaches for finding best predictive epidermal growth factor r…
A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography: MFP-Unet
Shakiba Moradi, Mostafa Ghelich-Oghli, Azin Alizadehasl +5
Segmentation of the Left ventricle (LV) is a crucial step for quantitative measurements such as area, volume, and ejection fraction. However, the automatic LV segmentation in 2D ec…