Abstract
This paper proposes a novel deep neural network architecture to effectively localize potential biomarkers in medical images, when only the image-level labels are available during model training. The proposed architecture combines a CNN classifier and a generative adversarial network (GAN) in a novel way, such that the CNN classifier and the discriminator in the GAN can effectively help the encoder-decoder in the GAN to remove biomarkers. Biomarkers in abnormal images can then be easily localized and segmented by subtracting the output of the encoder-decoder from its original input. The proposed approach was evaluated on diabetic retinopathy images with real biomarkers and on skin images with simulated biomarkers, showing state-of-the-art performance in localizing biomarkers even if biomarkers are irregularly scattered and are of various sizes in images.
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
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
Codella, N.C., Gutman, D., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: ISBI (2018)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)
Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)
Kandemir, M., Zhang, C., Hamprecht, F.A.: Empowering multiple instance histopathology cancer diagnosis by cell graphs. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 228–235. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_29
Manivannan, S., Cobb, C., Burgess, S., Trucco, E.: Subcategory classifiers for multiple-instance learning and its application to retinal nerve fiber layer visibility classification. IEEE TMI 36(5), 1140–1150 (2017)
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: NIPS (1998)
Rajpurkar, P., Irvin, J., Zhu, K., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
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
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., et al.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV (2017)
Yang, C., Rangarajan, A., Ranka, S.: Visual explanations from deep 3D convolutional neural networks for Alzheimer’s disease classification. arXiv preprint arXiv:1803.02544 (2018)
Zhang, Z., Xie, Y., Xing, F., McGough, M., Yang, L.: MDNet: a semantically and visually interpretable medical image diagnosis network. In: CVPR (2017)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)
Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: prediction difference analysis. arXiv preprint arXiv:1702.04595 (2017)
Acknowledgement
This work is supported in part by the National Key Research and Development Program (grant No. 2018YFC1315402, No. 2018YFC0116500), the Guangdong Key Research and Development Program (grant No. 2019B020228001), and the National Natural Science Foundation of China (grant No. 81770967, No. 91846109).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, R. et al. (2019). Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network. 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_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-32239-7_24
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)
