A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models

@article{Vento2022APA,
  title={A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models},
  author={Anthony Vento and Qingyu Zhao and Robert Paul and Kilian M. Pohl and Ehsan Adeli},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2022},
  volume={13433},
  pages={
          387-397
        }
}
  • Anthony VentoQingyu Zhao E. Adeli
  • Published 11 July 2022
  • Computer Science
  • Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Translating the use of modern machine learning algorithms into clinical applications requires settling challenges related to explain-ability and management of nuanced confounding factors. To suitably interpret the results, removing or explaining the effect of confounding variables (or metadata) is essential. Confounding variables affect the relationship between input training data and target outputs. Accordingly, when we train a model on such data, confounding variables will bias the… 

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