Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies

Convolutional Neural Network Addresses the Confounding Impact of CT Reconstruction Kernels on Radiomics Studies

Achieving high feature reproducibility while preserving biological information is one of the main challenges for the generalizability of current radiomics studies. Non-clinical imaging variables, such as reconstruction kernels, have shown to significantly impact radiomics features. In this study, we...

Full description

Bibliographic Details
Main Authors: Jin H. Yoon, Shawn H. Sun, Manjun Xiao, Hao Yang, Lin Lu, Yajun Li, Lawrence H. Schwartz, Binsheng Zhao
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Tomography
Subjects:
radiomics
reproducibility
convolutional neural network
computed tomography
kernel conversion
quantitative imaging
Online Access:https://www.mdpi.com/2379-139X/7/4/74

Internet

https://www.mdpi.com/2379-139X/7/4/74

Similar Items