A pipeline for the implementation and visualization of explainable machine learning for medical imaging using radiomics features

HIGHLIGHTS

• who: Cameron Severn and collaborators from the Department of Biostatistics and Informatics, University of Colorado, Aurora, CO, USA have published the paper: A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features, in the Journal: Sensors 2022, 5205 of /2022/
• what: The authors demonstrate the use of this workflow for developing and explaining prediction model using MRI data from glioma patients to predict genetic mutation. In this proposal, the authors explore the implementation of machine_learning to to improve transparency of model decision-making. While the authors focused on . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.