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

Authors:
Tomi Kauppi;Joni-Kristian Kamarainen;Lasse Lensu;Valentina Kalesnykiene;Iiris Sorri;Heikki Kälviäinen;Hannu Uusitalo;Juhani Pietilä
Affiliations:
Machine Vision and Pattern Recognition Research Group (MVPR),;MVPR/Computational Vision Group, Kouvola Department of Information Technology, Lappeenranta University of Technology (LUT), Finland;Machine Vision and Pattern Recognition Research Group (MVPR),;Department of Ophthalmology, University of Kuopio, Finland;Department of Ophthalmology, University of Kuopio, Finland;Machine Vision and Pattern Recognition Research Group (MVPR),;Department of Ophthalmology, University of Tampere, Finland;Perimetria Ltd., Finland
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

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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.