Calibration of A Regression Network Based on the Predictive Variance with Applications to Medical Images

Lior Frenkel, Jacob Goldberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Calibrating regression neural networks is crucial in medical imaging applications where the decision-making depends on the predicted confidence. In this study we propose a calibration procedure for regression networks that is based on scaling the predictive variance. The amount of scaling depends on the input and is a function of the predictive variance computed by the network. We thus find an entire calibration function instead of a single parameter. We report extensive experiments on a variety of image datasets and network architectures. Our approach achieves state-of-the-art results with a guarantee that the prediction accuracy is not altered.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
DOIs
StatePublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: 18 Apr 202321 Apr 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2023-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/TerritoryColombia
CityCartagena
Period18/04/2321/04/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Expected Normalized Calibration Error (ENCE)
  • network calibration
  • neural networks
  • regression

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