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Perspectives on Assurance Case Development for Retinal Disease Diagnosis Using Deep Learning

  • Chiara PicardiEmail author
  • Ibrahim Habli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)

Abstract

We report our experience with developing an assurance case for a deep learning system used for retinal disease diagnosis and referral. We investigate how an assurance case could clarify the scope and structure of the primary argument and identify sources of uncertainty. We also explore the need for an assurance argument pattern that could provide developers with a reusable template for communicating and structuring the different claims and evidence and clarifying the clinical context rather than merely focusing on meeting or exceeding performance measures.

Keywords

Assurance case Machine learning Retinal disease Safety 

References

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of YorkYorkUK

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