Reza Iranzad
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
Also affiliated: Sharif University of Technology (2021); FedEx (United States) (2024)
Formerly Arkansas Affiliated with University of Arkansas through 2024.
Unknown Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Reza Iranzad's research focuses on the application of advanced statistical learning and image processing techniques, particularly within the domain of medical imaging. He has investigated gradient boosted trees for spatial data analysis, with applications extending to medical imaging data and fluorescence intravital microscopy for detecting multicellular aggregates. His work also includes a review of random forest-based feature selection methods and their relevance to data science education and applications. Iranzad has explored multitask learning radiomics on longitudinal imaging to predict survival outcomes in non-small cell lung cancer and developed a new model for operating room scheduling. His research interests encompass tree-based ensemble statistical learning for spatial data and image processing for medical imaging.
Metrics
- h-index: 4
- Publications: 7
- Citations: 290
Selected Publications
-
Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy (2024)
-
Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer (2022)
-
Gradient boosted trees for spatial data and its application to medical imaging data (2021)
Collaboration Network
Top Collaborators
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer
- Gradient boosted trees for spatial data and its application to medical imaging data
- Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy
- Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy
- Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy
Similar Researchers
Based on overlapping research topics