Jake Qualls Source Confirmed

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

Researcher

Arkansas State University

faculty

8 h-index 18 pubs 369 cited

Research Areas

Medical Imaging Techniques and Applications Advanced Neural Network Applications Genomics and Phylogenetic Studies SARS-CoV-2 and COVID-19 Research Health Sciences Research and Education Bioinformatics and Genomic Networks Alzheimer's disease research and treatments Cancer Genomics and Diagnostics

Links

Lab Website

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OverviewAI-generated summary

Jake Qualls' research focuses on the application of computational methods and machine learning, particularly deep learning techniques, to medical imaging and biological data analysis. His work includes developing and evaluating ensemble U-Net models for kidney tumor segmentation using CT images, and utilizing transfer learning for COVID-19 diagnosis from chest X-rays. He has also investigated the combination of brain MRI imaging with other data types to improve Alzheimer's disease diagnosis and studied the severity of COVID-19 in emergency room patients using chest X-ray images.

Further research interests include identifying differentially expressed genes using large background sample sets. Qualls is a Co-PI on a National Science Foundation grant totaling $1,999,484, focused on "Understanding Invasion and Disease Ecology and Evolution through Computational Data Education." He has an active lab website and maintains collaborations with researchers at Arkansas State University, including Jason Causey and Jennifer Fowler, with whom he has co-authored multiple publications. His scholarly contributions are reflected in an h-index of 8, with 18 total publications and 369 citations.

Metrics

  • h-index: 8
  • Publications: 18
  • Citations: 369

Selected Publications

  • 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
  • Identify differentially expressed genes with large background samples (2021) DOI
  • An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images (2021) DOI

Federal Grants 1 $1,999,484 total

NSF Co-PI

Understanding Invasion and Disease Ecology and Evolution through Computational Data Education

NSF Research Traineeship (NRT) $1,999,484

Collaborators

Researchers in the database who share publications

Jason Causey Arkansas State University 6 shared publications Jennifer Fowler Arkansas State University 5 shared publications Emily S. Bellis Arkansas State University 1 shared publication 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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