Match tier
Confirmed
Presence
Current · Arkansas
Last published
2024
Sources
OpenAlex · ORCID
Refreshed
2026-08-15
J
Jason Causey
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Associate Professor
Also affiliated: National University of Defense Technology (2016); University of Arkansas Medical Center (2016); Arkansas Biosciences Institute (2020)
Faculty Researcher
12 h-index
37 pubs
550 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Jason Causey's research program investigates the application of advanced computational techniques, including deep learning and machine learning, to diverse scientific challenges. His work spans medical imaging analysis, such as segmenting kidney tumors from CT scans and classifying sex from 3D skull images, as well as agricultural applications like predicting rice yield variation using drone imagery. Causey has also contributed to studies on predicting COVID-19 diagnosis and hospitalization, and on genotype-by-environment interactions for maize yield estimation. His scholarship metrics include an h-index of 12, with 37 total publications and 512 citations. He frequently collaborates with researchers at Arkansas State University, including Jake Qualls, Jennifer Fowler, and Emily S. Bellis, with whom he has co-authored multiple publications.
Metrics
- h-index: 12
- Publications: 37
- Citations: 550
Selected Publications
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Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates (2024)
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Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction (2024)
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Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates (2024)
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Sex classification of 3D skull images using deep neural networks (2024)
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Single protein encapsulated SN38 for tumor-targeting treatment (2023)
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Single Protein Encapsulated SN38 for Tumor-Targeting Treatment (2023)
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Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images (2022)
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Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis (2022)
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COVID19 Diagnosis Using Chest X-rays and Transfer Learning (2022)
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Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning (2022)
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Identify differentially expressed genes with large background samples (2021)
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A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization (2021)
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An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images (2021)
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Transfer learning with chest X-rays for ER patient classification (2020)
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Spatial Pyramid Pooling With 3D Convolution Improves Lung Cancer Detection (2020)
Collaboration Network
Top Collaborators
Xiuzhen Huang
Cedars-Sinai Medical Center (US)
9 shared publications
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Single protein encapsulated SN38 for tumor-targeting treatment
- Sex classification of 3D skull images using deep neural networks
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
Showing 5 of 9 shared publications
Jonathan Stubblefield
Arkansas State University (US)
6 shared publications
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- Sex classification of 3D skull images using deep neural networks
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
Showing 5 of 6 shared publications
Jake Qualls
Arkansas State University
5 shared publications
In database
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
Jennifer Fowler
Arkansas State University
5 shared publications
In database
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
Karl Walker
University of Arkansas at Pine Bluff (US)
4 shared publications
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Identify differentially expressed genes with large background samples
Emily S. Bellis
Arkansas State University
3 shared publications
In database
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction
Yuanfang Guan
University of Michigan–Ann Arbor (US)
2 shared publications
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- Identify differentially expressed genes with large background samples
Ahmed A. Hashem
Suez Canal University
2 shared publications
In database
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction
Changjun Yu
California Institute of Technology (US)
2 shared publications
- Single protein encapsulated SN38 for tumor-targeting treatment
- Single Protein Encapsulated SN38 for Tumor-Targeting Treatment
Faqing Huang
University of Southern Mississippi (US)
2 shared publications
- Single protein encapsulated SN38 for tumor-targeting treatment
- Single Protein Encapsulated SN38 for Tumor-Targeting Treatment
Kinsley Wang
Aptose Biosciences (United States) (US)
2 shared publications
- Single protein encapsulated SN38 for tumor-targeting treatment
- Single Protein Encapsulated SN38 for Tumor-Targeting Treatment
Warren Chow
2 shared publications
- Single protein encapsulated SN38 for tumor-targeting treatment
- Single Protein Encapsulated SN38 for Tumor-Targeting Treatment
Xiang Ling
Canget BioTekpharma (United States) (US)
2 shared publications
- Single protein encapsulated SN38 for tumor-targeting treatment
- Single Protein Encapsulated SN38 for Tumor-Targeting Treatment
Fengzhi Li
Roswell Park Comprehensive Cancer Center (US)
2 shared publications
- Single protein encapsulated SN38 for tumor-targeting treatment
- Single Protein Encapsulated SN38 for Tumor-Targeting Treatment
Galen Cook‐Wiens
Cedars-Sinai Medical Center (US)
2 shared publications
- Single protein encapsulated SN38 for tumor-targeting treatment
- Single Protein Encapsulated SN38 for Tumor-Targeting Treatment
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