44 citations · 75 across the 7 of their papers we have counts for
7 papers
Quantifying U-Net Uncertainty in Multi-Parametric MRI-based Glioma Segmentation by Spherical Image Projection
Zhenyu Yang, Kyle Lafata, Eugene Vaios +4
The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image…
A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation
Zhenyu Yang, Zongsheng Hu, Hangjie Ji +4
Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) based glioma segment…
A Deep Learning Model with Radiomics Analysis Integration for Glioblastoma Post-Resection Survival Prediction
Zongsheng Hu, Zhenyu Yang, Haozhao Zhang +4
Purpose: To develop a novel deep-learning model that integrates radiomics analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM) post-resection survival pre…
A Radiomics-Boosted Deep-Learning Model for COVID-19 and Non-COVID-19 Pneumonia Classification Using Chest X-ray Image
Zongsheng Hu, Zhenyu Yang, Kyle J. Lafata +2
To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-lear…
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application
Hangjie Ji, Kyle Lafata, Yvonne Mowery +4
This paper develops a method of biologically guided deep learning for post-radiation FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose informatio…
Quantification of lung function on CT images based on pulmonary radiomic filtering
Zhenyu Yang, Kyle J Lafata, Xinru Chen +4
Purpose: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung CT. Methods: The lung volume was segmented…
Data clustering based on Langevin annealing with a self-consistent potential
Kyle Lafata, Zhennan Zhou, Jian-Guo Liu +1
This paper introduces a novel data clustering algorithm based on Langevin dynamics, where the associated potential is constructed directly from the data. To introduce a self-consis…