Jake Qualls
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Researcher
Also affiliated: Arkansas Biosciences Institute (2020)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Jake Qualls' research focuses on the application of advanced computational methods, particularly deep learning and neural networks, to medical imaging for disease diagnosis and segmentation. His work includes developing ensemble U-Net models for kidney tumor segmentation using CT images and exploring the use of transfer learning for COVID-19 diagnosis from chest X-rays.
Qualls also investigates the combination of brain MRI imaging with other data types to improve Alzheimer's disease diagnosis. He has studied the severity of COVID-19 in patients admitted to emergency rooms using chest X-ray images. His research extends to identifying differentially expressed genes within large sample sets. Qualls is a Co-Principal Investigator on an NSF grant totaling $1,999,484, focused on understanding invasion and disease ecology and evolution through computational data education. He collaborates with researchers at Arkansas State University, including Jason Causey and Jennifer Fowler, and with Dakota S. Dale at the University of Arkansas at Fayetteville.
Metrics
- h-index: 8
- Publications: 18
- Citations: 393
Selected Publications
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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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Identify differentially expressed genes with large background samples (2021)
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An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images (2021)
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Massard Prairie Restoration and Soil Microbiome Succession (2020)
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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)
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Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification (2020)
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Minor QTLs mining through the combination of GWAS and machine learning feature selection (2019)
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Minor QTLs mining through the combination of GWAS and machine learning feature selection (2019)
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Arkansas AI-Campus Method for the 2019 Kidney Tumor Segmentation Challenge (2019)
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Minor QTLs mining through the combination of GWAS and machine learning feature selection (2019)
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CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis (2019)
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Highly accurate model for prediction of lung nodule malignancy with CT scans (2018)
Federal Grants 1 $1,999,484 total
Understanding Invasion and Disease Ecology and Evolution through Computational Data Education
Collaboration Network
Top Collaborators
- 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
- 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
- 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
- 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
- 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
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- Identify differentially expressed genes with large background samples
- An Ensemble of U-Net Models for Kidney Tumor Segmentation With CT Images
- Identify differentially expressed genes with large background samples
- Identify differentially expressed genes with large background samples
- COVID19 Diagnosis Using Chest X-rays and Transfer Learning
- 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
- Study the combination of brain MRI imaging and other datatypes to improve Alzheimer’s disease diagnosis
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
- Study COVID-19 Severity of Patients Admitted to Emergency Room (ER) with Chest X-ray Images
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