Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks

  • Hendrik BurwinkelEmail author
  • Anees Kazi
  • Gerome Vivar
  • Shadi Albarqouni
  • Guillaume Zahnd
  • Nassir Navab
  • Seyed-Ahmad Ahmadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11769)


Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as age and gender to model patient cohort relations in a graph structure. Concepts from graph signal processing are leveraged to learn the optimal mapping of multi-modal features, e.g. from images to disease classes. Related studies so far have considered image features that are extracted in a pre-processing step. We hypothesize that such an approach prevents the network from optimizing feature representations towards achieving the best performance in the graph network. We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly. We validate this architecture against state-of-the-art inductive graph networks and demonstrate significantly improved classification scores on a modified MNIST toy dataset, as well as comparable classification results with higher stability on a chest X-ray image dataset. Additionally, we explain how the structural information of the graph affects both the image filters and the feature learning.


Graph convolutional networks Representation learning Disease classification 



The study was supported by the Carl Zeiss Meditec AG in Oberkochen, Germany, and the German Federal Ministry of Education and Research (BMBF) in connection with the foundation of the German Center for Vertigo and Balance Disorders (DSGZ) (grant number 01 EO 0901). Further, we thank NVIDIA Corporation for the sponsoring of a Titan V GPU.


  1. 1.
    Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Sig. Process. Mag. 34(4), 18–42 (2017)CrossRefGoogle Scholar
  2. 2.
    Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. NIPS (2016)Google Scholar
  3. 3.
    Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. NIPS (2017)Google Scholar
  4. 4.
    Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K.: Abnormality detection and localization in chest X-rays using deep convolutional neural networks (2017).
  5. 5.
    Kazi, A., Krishna, S.A., Shekarforoush, S., Kortuem, K., Albarqouni, S., Navab, N.: Self-attention equipped graph convolutions for disease prediction. ISBI (2019)Google Scholar
  6. 6.
    Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. ICLR (2017)Google Scholar
  7. 7.
    Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., Bronstein, M.: Geo. deep learning on graphs and manifolds using mixture model CNNs. CVPR (2017)Google Scholar
  8. 8.
    Parisot, S., et al.: Spectral graph convolutions for population-based disease prediction. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 177–185. Springer, Cham (2017). Scholar
  9. 9.
    Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning (2017).
  10. 10.
    Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. ICLR (2018)Google Scholar
  11. 11.
    Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. CVPR (2017)Google Scholar
  12. 12.
    Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. CVPR (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hendrik Burwinkel
    • 1
    Email author
  • Anees Kazi
    • 1
  • Gerome Vivar
    • 2
  • Shadi Albarqouni
    • 1
  • Guillaume Zahnd
    • 1
  • Nassir Navab
    • 1
    • 3
  • Seyed-Ahmad Ahmadi
    • 2
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGarching bei MünchenGermany
  2. 2.German Center for Vertigo and Balance Disorders, Ludwig-Maximilians Universität MünchenMünchenGermany
  3. 3.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations