44 citations · 74 across the 10 of their papers we have counts for
11 papers
A Dual Radiomic and Dosiomic Filtering Technique for Locoregional Radiation Pneumonitis Prediction in Breast Cancer Patients
Zhenyu Yang, Qian Chen, Rihui Zhang +8
Purpose: Radiation pneumonitis (RP) is a serious complication of intensity-modulated radiation therapy (IMRT) for breast cancer patients, underscoring the need for precise and expl…
A Voxel-Wise Uncertainty-Guided Framework for Glioma Segmentation Using Spherical Projection-Based U-Net and Localized Refinement in Multi-Parametric MRI
Zhenyu Yang, Chen Yang, Rihui Zhang +3
Purpose: Accurate segmentation of glioma subregions in multi-parametric MRI (MP-MRI) is essential for diagnosis and treatment planning but remains challenging due to tumor heteroge…
Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification
Zhenyu Yang, Haiming Zhu, Rihui Zhang +5
Background: Deep learning has significantly advanced medical image analysis, with Vision Transformers (ViTs) offering a powerful alternative to convolutional models by modeling lon…
An Explainable Neural Radiomic Sequence Model with Spatiotemporal Continuity for Quantifying 4DCT-based Pulmonary Ventilation
Rihui Zhang, Haiming Zhu, Jingtong Zhao +4
Accurate evaluation of regional lung ventilation is essential for the management and treatment of lung cancer patients, supporting assessments of pulmonary function, optimization o…
Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation
Tianhao Li, Tianyu Zeng, Yujia Zheng +6
Deep learning-based medical image segmentation models, such as U-Net, rely on high-quality annotated datasets to achieve accurate predictions. However, the increasing use of genera…
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation
Yang Chen, Zhenyu Yang, Jingtong Zhao +4
We developed a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). This model wa…
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…
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…