3 citations · 3 across the 5 of their papers we have counts for
5 papers
Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation
Abhishek K Dubey, Michael T Young, Christopher Stanley +2
Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a larg…
Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports
Abhishek K Dubey, Alina Peluso, Jacob Hinkle +2
Shallow Convolution Neural Network (CNN) is a time-tested tool for the information extraction from cancer pathology reports. Shallow CNN performs competitively on this task to othe…
Automated detection of corrosion in used nuclear fuel dry storage canisters using residual neural networks
Theodore Papamarkou, Hayley Guy, Bryce Kroencke +8
Nondestructive evaluation methods play an important role in ensuring component integrity and safety in many industries. Operator fatigue can play a critical role in the reliability…
Challenges in Markov chain Monte Carlo for Bayesian neural networks
Theodore Papamarkou, Jacob Hinkle, M. Todd Young +1
Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the para…
Learning nonlinear level sets for dimensionality reduction in function approximation
Guannan Zhang, Jiaxin Zhang, Jacob Hinkle
We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variet…