Jason Causey Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Associate Professor

Arkansas State University

faculty

12 h-index 37 pubs 505 cited

Research Areas

Advanced Neural Network Applications Medical Imaging Techniques and Applications Radiomics and Machine Learning in Medical Imaging Rice Cultivation and Yield Improvement SARS-CoV-2 and COVID-19 Research Genomics and Phylogenetic Studies Plant Physiology and Cultivation Studies Cancer-related molecular mechanisms research

Links

ORCID Lab Website

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OverviewAI-generated summary

Jason Causey's research utilizes advanced computational methods, including deep learning and neural networks, across diverse scientific domains. His work includes developing models for medical image segmentation, such as for kidney tumors in CT scans and sex classification from 3D skull images. He has also applied these techniques to analyze agricultural data, contributing to efforts in predicting maize yield and detecting intra-field variations in rice production using UAV imagery. Causey has been involved in research related to COVID-19, focusing on diagnosis using chest X-rays and developing predictive models for hospitalization. His scholarship metrics include an h-index of 12 with over 500 citations across 37 publications. Causey frequently collaborates with researchers at Arkansas State University, including Jake Qualls, Jennifer Fowler, and Emily S. Bellis.

Metrics

  • h-index: 12
  • Publications: 37
  • Citations: 505

Selected Publications

  • Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates (2024) DOI
  • Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction (2024) DOI
  • Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates (2024) DOI
  • Sex classification of 3D skull images using deep neural networks (2024) DOI
  • Single protein encapsulated SN38 for tumor-targeting treatment (2023) DOI
  • Single Protein Encapsulated SN38 for Tumor-Targeting Treatment (2023) DOI
  • Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images (2022) DOI
  • Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis (2022) DOI
  • COVID19 Diagnosis Using Chest X-rays and Transfer Learning (2022) DOI
  • Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning (2022) DOI
  • Identify differentially expressed genes with large background samples (2021) DOI
  • A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization (2021) DOI
  • An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images (2021) DOI

Collaborators

Researchers in the database who share publications

Jake Qualls Arkansas State University 6 shared publications Jennifer Fowler Arkansas State University 5 shared publications Emily S. Bellis Arkansas State University 5 shared publications Ahmed A. Hashem Arkansas State University 2 shared publications Dakota S. Dale University of Arkansas at Fayetteville 1 shared publication Alan Kronberger Arkansas State University 1 shared publication Anna De Feria Arkansas State University 1 shared publication Abhijit Shivkumar Arkansas State University 1 shared publication

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