Abstract
The past few years have witnessed the great success of applying deep neural networks (DNNs) in computer-aided diagnosis. However, little attention has been paid to provide pathological evidence in the existing DNNs for medical diagnosis. In fact, feature visualization in DNNs is able to help understanding how the computer make decisions, and thus it shows promise on finding pathological evidence from computer-aided diagnosis. In this paper, we propose a novel pathology-aware visualization approach for DNN-based glaucoma classification, which is used to locate the pathological evidence from fundus images for glaucoma. Besides, we apply the visualization framework to the glaucoma images synthesis task, through which specific pathological areas of synthesized images can be enhanced. Finally, experimental results show that the visualization heat maps can pinpoint different glaucoma pathologies with high accuracy, and that the generated glaucoma images are more pathophysiologically clear in rim loss (RL) and retinal neural fiber layer damage (RNFLD), which is verified by the ophthalmologist.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)
Chen, X., Xu, Y., Wong, D.W.K., Wong, T.Y., Liu, J.: Glaucoma detection based on deep convolutional neural network. In: EMBC 2015, pp. 715–718. IEEE (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR 2016, pp. 2921–2929 (2016)
Van Molle, P., De Strooper, M., Verbelen, T., Vankeirsbilck, B., Simoens, P., Dhoedt, B.: Visualizing convolutional neural networks to improve decision support for skin lesion classification. In: Stoyanov, D., et al. (eds.) MLCN/DLF/IMIMIC -2018. LNCS, vol. 11038, pp. 115–123. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02628-8_13
Rieke, J., Eitel, F., Weygandt, M., Haynes, J.-D., Ritter, K.: Visualizing convolutional networks for MRI-based diagnosis of alzheimer’s disease. In: Stoyanov, D., et al. (eds.) MLCN/DLF/IMIMIC -2018. LNCS, vol. 11038, pp. 24–31. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02628-8_3
Wang, X., et al.: Weakly supervised learning for whole slide lung cancer image classification. In: MIDL (2018)
Gondal, W.M., Köhler, J.M., Grzeszick, R., Fink, G.A., Hirsch, M.: Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. In: ICIP, pp. 2069–2073. IEEE (2017)
Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformarle image registration. In: ISBI (2018)
Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ayed, I.B.: Constrained-cnn losses for weakly supervised segmentation. MIA 54, 88–99 (2019)
Li, L., Xu, M., Wang, X., Jiang, L., Liu, H.: Attention based glaucoma detection: a large-scale database with a CNN model. arXiv preprint arXiv:1903.10831 (2019)
Xiancheng, W., et al.: Retina blood vessel segmentation using a U-net based convolutional neural network. In: ICDS 2018, Beijing, China, 8–9 June 2018 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Smilkov, D., Thorat, N., Kim, B., Viégas, F., Wattenberg, M.: SmoothGrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)
Acknowledgments
This work was supported by the NSFC projects 61876013, 61922009, 61573037, and by BMSTC under Grant Z181100001918035.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Xu, M., Li, L., Wang, Z., Guan, Z. (2019). Pathology-Aware Deep Network Visualization and Its Application in Glaucoma Image Synthesis. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-32239-7_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32238-0
Online ISBN: 978-3-030-32239-7
eBook Packages: Computer ScienceComputer Science (R0)
