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Fusion of Multiple Expert Annotations and Overall Score Selection for Medical Image Diagnosis

  • Tomi Kauppi
  • Joni-Kristian Kamarainen
  • Lasse Lensu
  • Valentina Kalesnykiene
  • Iiris Sorri
  • Heikki Kälviäinen
  • Hannu Uusitalo
  • Juhani Pietilä
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)

Abstract

Two problems especially important for supervised learning and classification in medical image processing are addressed in this study: i) how to fuse medical annotations collected from several medical experts and ii) how to form an image-wise overall score for accurate and reliable automatic diagnosis. Both of the problems are addressed by applying the same receiver operating characteristic (ROC) framework which is made to correspond to the medical practise. The first problem arises from the typical need to collect the medical ground truth from several experts to understand the underlying phenomenon and to increase robustness. However, it is currently unclear how these expert opinions (annotations) should be combined for classification methods. The second problem is due to the ultimate goal of any automatic diagnosis, a patient-based (image-wise) diagnosis, which consequently must be the ultimate evaluation criterion before transferring any methods into practise. Various image processing methods provide several, e.g., spatially distinct, results, which should be combined into a single image-wise score value. We discuss and investigate these two problems in detail, propose good strategies and report experimental results on a diabetic retinopathy database verifying our findings.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tomi Kauppi
    • 1
  • Joni-Kristian Kamarainen
    • 2
  • Lasse Lensu
    • 1
  • Valentina Kalesnykiene
    • 3
  • Iiris Sorri
    • 3
  • Heikki Kälviäinen
    • 1
  • Hannu Uusitalo
    • 4
  • Juhani Pietilä
    • 5
  1. 1.Machine Vision and Pattern Recognition Research Group (MVPR)Finland
  2. 2.MVPR/Computational Vision Group, Kouvola Department of Information TechnologyLappeenranta University of Technology (LUT)Finland
  3. 3.Department of OphthalmologyUniversity of KuopioFinland
  4. 4.Department of OphthalmologyUniversity of TampereFinland
  5. 5.Perimetria Ltd.Finland

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