Empirical validation of Conformal Prediction for trustworthy skin lesions classification.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Uncertainty quantification is a pivotal field that contributes to realizing reliable and robust systems. It becomes instrumental in fortifying safe decisions by providing complementary information, particularly within high-risk applications. existing studies have explored various methods that often operate under specific assumptions or necessitate substantial modifications to the network architecture to effectively account for uncertainties. The objective of this paper is to study Conformal Prediction, an emerging distribution-free uncertainty quantification technique, and provide a comprehensive understanding of the advantages and limitations inherent in various methods within the medical imaging field.

Authors

  • Jamil Fayyad
    University of Victoria, 800 Finnerty Road, Victoria, V8P 5C2, BC, Canada; Cognia AI, 2031 Store street, Victoria, V8T 5L9, BC, Canada. Electronic address: jfayyad@uvic.ca.
  • Shadi Alijani
    University of Victoria, 800 Finnerty Road, Victoria, V8P 5C2, BC, Canada; Cognia AI, 2031 Store street, Victoria, V8T 5L9, BC, Canada. Electronic address: shadialijani@uvic.ca.
  • Homayoun Najjaran
    University of Victoria, 800 Finnerty Road, Victoria, V8P 5C2, BC, Canada; Cognia AI, 2031 Store street, Victoria, V8T 5L9, BC, Canada. Electronic address: najjaran@uvic.ca.