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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.


CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

Open Access
Online
ISSN
2364-5504
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Integrating Label Uncertainty in Ultrasound Image Classification using Weighted Support Vector Machines

Jannis Hagenah
  • Corresponding author
  • Institute for Robotics and Cognitive Systems, University of Lubeck, Ratzeburger Allee 160, Lubeck, Germany
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/ Sascha Leymann
  • Institute for Robotics and Cognitive Systems, University of Lubeck, Ratzeburger Allee 160, Lubeck, Germany
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/ Floris Ernst
  • Institute for Robotics and Cognitive Systems, University of Lubeck, Ratzeburger Allee 160, Lubeck, Germany
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Published Online: 2019-09-18 | DOI: https://doi.org/10.1515/cdbme-2019-0072

Abstract

Inference from medical image data using machine learning still suffers from the disregard of label uncertainty. Usually, medical images are labeled by multiple experts. However, the uncertainty of this training data, assessible as the unity of opinions of observers, is neglected as training is commonly performed on binary decision labels. In this work, we present a novel method to incorporate this label uncertainty into the learning problem using weighted Support Vector Machines (wSVM). The idea is to assign an uncertainty score to each data point. The score is between 0 and 1 and is calculated based on the unity of opinions of all observers, where u = 1 if all observers have the same opinion and u = 0 if the observers opinions are exactly 50/50, with linear interpolation in between. This score is integrated in the Support Vector Machine (SVM) optimization as a weighting of errors made for the corresponding data point. For evaluation, we asked 15 observers to label 48 2D ultrasound images of aortic roots addressing whether the images show a healthy or a pathologically dilated anatomy, where the ground truth was known. As the observers were not trained experts, a high diversity of opinions was present in the data set. We performed image classification using both approaches, i.e. classical SVM and wSVM with integrated uncertainty weighting, utilizing 10-fold Cross Validation, respectively (linear kernel, C = 7). By incorporating the observer uncertainty, the classification accuracy could be improved by 3.1 percentage points (SVM: 83.5%, wSVM: 86.6%). This indicates that integrating information on the observers’ unity of opinions increases the generalization performance of the classifier and that uncertainty weighted wSVM could present a promising method for machine learning in the medical domain.

Keywords: machine learning; label uncertainty; image processing; ultrasound; classification

About the article

Published Online: 2019-09-18

Published in Print: 2019-09-01


Citation Information: Current Directions in Biomedical Engineering, Volume 5, Issue 1, Pages 285–287, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2019-0072.

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© 2019 by Walter de Gruyter Berlin/Boston. This work is licensed under the Creative Commons Attribution 4.0 Public License. BY 4.0

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