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As easy as 1, 2... 4? Uncertainty in counting tasks for medical imaging

Research output: Contribution to journalArticle

Zach Eaton-Rosen, Thomas Varsavsky, Sebastien Ourselin, M. Jorge Cardoso

Original languageEnglish
Pages (from-to)356-364
Number of pages9
JournalMedical Image Computing and Computer Assisted Intervention -- MICCAI
DOIs
Publication statusPublished - 25 Jul 2019

Bibliographical note

Early Accept to MICCAI 2019

Documents

  • 1907.11555v1

    1907.11555v1.pdf, 2.99 MB, application/pdf

    23/10/2019

King's Authors

Abstract

Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and suggest ways of deriving predictive intervals from these. We then propose and test a method for calculating intervals as an output of a multi-task network. These predictive intervals are optimised to be as narrow as possible, while also enclosing a desired percentage of the data. We demonstrate the effectiveness of this technique on histopathological cell counting and white matter hyperintensity counting. Finally, we offer insight into other areas where this technique may apply.

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