Exploring the Similarity of Medical Imaging Classification Problems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10552)


Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning – predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community.


  1. 1.
    Vilalta, R., Drissi, Y.: A perspective view and survey of meta-learning. Artif. Intell. Rev. 18, 77–95 (2002)CrossRefGoogle Scholar
  2. 2.
    Duin, R.P.W., Pekalska, E., Tax, D.M.J.: The characterization of classification problems by classifier disagreements. Int. Conf. Pattern Recogn. 1, 141–143 (2004)Google Scholar
  3. 3.
    Cheplygina, V., Tax, D.M.J.: Characterizing multiple instance datasets. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 15–27. Springer, Cham (2015). doi: 10.1007/978-3-319-24261-3_2 CrossRefGoogle Scholar
  4. 4.
    Muenzing, S.E.A., van Ginneken, B., Viergever, M.A., Pluim, J.P.W.: DIRBoost-an algorithm for boosting deformable image registration: application to lung CT intra-subject registration. Med. Image Anal. 18(3), 449–459 (2014)CrossRefGoogle Scholar
  5. 5.
    Gurari, D., Jain, S.D., Betke, M., Grauman, K.: Pull the plug? predicting if computers or humans should segment images. In: Computer Vision and Pattern Recognition, pp. 382–391 (2016)Google Scholar
  6. 6.
    Cox, T.F., Cox, M.A.: Multidimensional Scaling. CRC Press, Boca Raton (2000)zbMATHGoogle Scholar
  7. 7.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  8. 8.
    Landman, B.A., et al.: MICCAI 2012 Workshop on Multi-Atlas Labeling. CreateSpace Independent Publishing Platform (2012)Google Scholar
  9. 9.
    Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J., Išgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRefGoogle Scholar
  10. 10.
    Veta, M., Van Diest, P.J., Willems, S.M., Wang, H., Madabhushi, A., Cruz-Roa, A., Gonzalez, F., Larsen, A.B., Vestergaard, J.S., Dahl, A.B., et al.: Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med. Image Anal. 20(1), 237–248 (2015)CrossRefGoogle Scholar
  11. 11.
    Veta, M., van Diest, P.J., Jiwa, M., Al-Janabi, S., Pluim, J.P.W.: Mitosis counting in breast cancer: object-level interobserver agreement and comparison to an automatic method. PLoS ONE 11(8), e0161286 (2016)CrossRefGoogle Scholar
  12. 12.
    Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  13. 13.
    Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P.W., Duits, R., ter Haar Romeny, B.M.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016)CrossRefGoogle Scholar
  14. 14.
    Dashtbozorg, B., Mendonça, A.M., Campilho, A.: An automatic graph-based approach for artery/vein classification in retinal images. IEEE Trans. Image Process. 23(3), 1073–1083 (2014)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Decencière, E., et al.: TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 34(2), 196–203 (2013)CrossRefGoogle Scholar
  16. 16.
    Vanschoren, J., Van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. ACM SIGKDD Explorations Newsletter 15(2), 49–60 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Medical Image Analysis, Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical InformaticsErasmus Medical CenterRotterdamThe Netherlands
  3. 3.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands

Personalised recommendations