Abstract
Image classification using deep convolutional neural networks (DCNN) has a competitive performance with other state-of-the-art methods. Fundus image classification into disease types is also a promising application domain of DCNN. Typically fundus image classifier is trained using fundus images with labels showing disease types. Such training data is relatively easy to obtain, but a massive number of training data is required to achieve adequate classification performance. If classifier can concentrate the evidential regions attached to training images, it is possible to boost the performance with a limited number of the training dataset. However, such regions are very hard to obtain, especially for fundus image classification because only professional ophthalmologist can give such regions and selecting such regions by GUI is very time-consuming. To boost the classification performance with significantly light ophthalmologist intervention, we propose a new method: first, we show evidential heatmaps by DCNN to ophthalmologists, and then obtained their feedback of selecting images with reasonable evidential regions. This intervention is far very easy for opthalmologist compared to drawing evidential regions. Experiments using fundus images revealed that our method improved accuracy from 90.1% to 94.5% in comparison with the existing method. We also found that the attention regions generated by our process are closer to the GT attention regions provided by ophthalmologists.
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References
Wykoff, C.C., Ou, W.C.: Age-related macular degeneration: 5 things to know (2018). https://www.medscape.com/viewarticle/906221
Zhang, X., Wei, Y., Feng, J., Yang, Y., Huang, T.S.: Adversarial complementary learning for weakly supervised object localization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1325–1334 (2018)
World Health Organization: Global report on diabetes, January 2016
Akram, M., Khalid, S., Khan, S.: Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit. 46, 107–116 (2013)
Bortolin Júnior, S., Welfer, D.: Automatic detection of microaneurysms and hemorrhages in color eye fundus images. Int. J. Comput. Sci. Inf. Technol. 5, 21–37 (2013)
Weinreb, R., Aung, T., Medeiros, F.: The pathophysiology and treatment of glaucoma a review. JAMA J. Am. Med. Assoc. 311, 1901–1911 (2014)
Liu, Y., Allingham, R.R.: Major review: molecular genetics of primary open-angle glaucoma. Exp. Eye Res. 160, 62–84 (2017)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: The IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Ravishankar, S., Jain, A., Mittal, A.: Automated feature extraction for early detection of diabetic retinopathy in fundus images. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 210–217 (2009)
Zhou, W., Wu, C., Yu, X., Gao, Y., Du, W.: Automatic fovea center localization in retinal images using saliency-guided object discovery and feature extraction. J. Med. Imaging Health Inform. 7, 1070–1077 (2017)
Muramatsu, C., Ishida, K., Sawada, A., Hatanaka, Y., Yamamoto, T., Fujita, H.: Automated detection of retinal nerve fiber layer defects on fundus images: false positive reduction based on vessel likelihood. In: Proceedings of the Medical Imaging 2016: Computer-Aided Diagnosis. SPIE, vol. 9785, p. 97852L (2016)
Waseem, S., Akram, M., Ahmed, B.: Drusen exudate lesion discrimination in colour fundus images. In: 2014 14th International Conference on Hybrid Intelligent Systems, HIS 2014, pp. 176–180 (2015)
Tang, L., Niemeijer, M., Reinhardt, J., Garvin, M., Abramoff, M.: Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans. Med. Imaging 32, 364–375 (2012)
Hatanaka, Y., et al.: CAD scheme to detect hemorrhages and exudates in ocular fundus images. In: Proceedings of the Medical Imaging 2007: Computer-Aided Diagnosis, vol. 6514, p. 65142M (2007)
Chen, X., Xu, Y., Yan, S., Wong, D.W.K., Wong, T.Y., Liu, J.: Automatic feature learning for glaucoma detection based on deep learning. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 669–677. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_80
Simonyan, K., et al.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR (2013)
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
Pathak, D., Krahenbuhl, P., Darrell, T.: Constrained convolutional neural networks for weakly supervised segmentation. In: The IEEE International Conference on Computer Vision (ICCV), pp. 1796–1804 (2015)
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: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2069–2073 (2017)
Li, K., Wu, Z., Peng, K.-C., Ernst, J., Fu, Y.: Tell me where to look: guided attention inference network. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 9215–9223 (2018)
Hwang, S., Kim, H.-E.: Self-transfer learning for weakly supervised lesion localization. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 239–246. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_28
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 2921–2929 (2016)
Zhang, J., Lin, Z., Brandt, J., Shen, X., Sclaroff, S.: Top-down neural attention by excitation backprop. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 543–559. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_33
Singh, K.K., Lee, Y.J.: Hide-and-seek: forcing a network to be meticulous for weakly-supervised object and action localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3544–3553 (2017)
Zhang, X., Wei, Y., Feng, J., Yang, Y., Huang, T.S.: Adversarial complementary learning for weakly supervised object localization. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 1325–1334 (2018)
Wei, Y., Feng, J., Liang, X., Cheng, M.-M., Zhao, Y., Yan, S.: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 1568–1576 (2017)
Kauppi, T., et al.: DIARETDB1 diabetic retinopathy database and evaluation protocol. In: Proceedings of the Medical Image Understanding and Analysis (MIUA) (2007)
Decencire, E., et al.: TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 34(2), 196–203 (2013)
Acknowledgement
The fundus image dataset is prepared and kindly provided by Japan Ocular Imaging Registry Research Group. This research is supported by the ICT infrastructure establishment and implementation of artificial intelligence for clinical and medical research from Japan Agency for Medical Research and development, AMED.
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Meng, Q., Hashimoto, Y., Satoh, S. (2020). Fundus Image Classification and Retinal Disease Localization with Limited Supervision. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_33
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