Abstract
Disease forecast is an effective solution to early treatment and prevention for some irreversible diseases, e.g., glaucoma. Different from existing disease detection methods that predict the current status of a patient, disease forecast aims to predict the future state for early treatment. This paper is a first attempt to address the glaucoma forecast task utilizing the sequential fundus images of a patient. Specifically, we establish a database of sequential fundus images for glaucoma forecast (SIGF), which includes an average of 9 images per eye, corresponding to 3,671 fundus images in total. Besides, a novel deep learning method for glaucoma forecast (DeepGF) is proposed based on our SIGF database, consisting of an attention-polar convolution neural network (AP-CNN) and a variable time interval long short-term memory (VTI-LSTM) network to learn the spatio-temporal transition at different time intervals across sequential medical images of a person. In addition, a novel active convergence (AC) training strategy is proposed to solve the imbalanced sample distribution problem of glaucoma forecast. Finally, the experimental results show the effectiveness of our DeepGF method in glaucoma forecast.
Liu Li and Xiaofei Wang contribute equally to this paper.
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The database is available at https://github.com/XiaofeiWang2018/DeepGF.
References
Amirkhan, R., Hoogendoorn, M., Numans, M.E., Moons, L.: Using recurrent neural networks to predict colorectal cancer among patients. In: SSCI, pp. 1–8. IEEE (2017)
Bourne, R.R.A., et al.: Causes of vision loss worldwide, 1990–2010: a systematic analysis. Lancet GH 1(6), e339–e349 (2013)
Chen, J., et al.: Detecting hypoglycemia incidents reported in patients’ secure messages: using cost-sensitive learning and oversampling to reduce data imbalance. JMIR 21(3), e11990 (2019)
Chen, X., Xu, Y., Wong, D.W.K., Wong, T.Y., Liu, J.: Glaucoma detection based on deep convolutional neural network. In: EMBC, p. 715 (2015)
Cheng, Y., Wang, F., Zhang, P., Hu, J.: Risk prediction with electronic health records: a deep learning approach. In: ICDM, pp. 432–440. SIAM (2016)
Fan, X., Wu, L.L., Ma, Z.Z., Xiao, G.G., Liu Jr., F.: Usefulness of frequency-doubling technology for perimetrically normal eyes of open-angle glaucoma patients with unilateral field loss. Ophthalmology 117(8), 1530–1537 (2010)
Fu, H., et al.: Disc-aware ensemble network for glaucoma screening from fundus image. IEEE TMI (2018)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)
Harizman, N., et al.: The ISNT rule and differentiation of normal from glaucomatous eyes. Arch. Ophthalmol. 124(11), 1579 (2006)
Jin, B., Che, C., Liu, Z., Zhang, S., Yin, X., Wei, X.: Predicting the risk of heart failure with EHR sequential data modeling. IEEE Access 6, 9256–9261 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krawczyk, B., Galar, M., Jeleń, Ł., Herrera, F.: Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. ASC 38, 714–726 (2016)
Le, T., Vo, M.T., Vo, B., Lee, M.Y., Baik, S.W.: A hybrid approach using oversampling technique and cost-sensitive learning for bankruptcy prediction. Complexity (2019)
Li, L., Xu, M., Wang, X., Jiang, L., Liu, H.: Attention based glaucoma detection: a large-scale database and CNN model. In: IEEE CVPR, pp. 10571–10580 (2019)
Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015)
Liu, H., et al.: Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. JAMA Ophthalmol. 137(12), 1353–1360 (2019)
Tham, Y.C., Li, X., Wong, T.Y., Quigley, H.A., Aung, T., Cheng, C.Y.: Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 121(11), 2081 (2014)
Thomson, R., Luettel, D., Healey, F., Scobie, S.: Safer Care for the Acutely Ill Patient: Learning from Serious Incidents. National Patient Safety Agency, London (2007)
Tomašev, N., et al.: A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572(7767), 116 (2019)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: IEEE CVPR, pp. 2921–2929 (2016)
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This paper is supported by BMSTC project under grant Z181100001918035, and by the NSFC project under grant 61922009 and 61876013.
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Li, L., Wang, X., Xu, M., Liu, H., Chen, X. (2020). DeepGF: Glaucoma Forecast Using the Sequential Fundus Images. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_60
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