DeepGF: Glaucoma Forecast Using the Sequential Fundus Images

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.

[1]  Hong Yu,et al.  Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance , 2019, Journal of medical Internet research.

[2]  Xiaofei Wang,et al.  Attention Based Glaucoma Detection: A Large-Scale Database and CNN Model , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Xiaochun Cao,et al.  Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image , 2018, IEEE Transactions on Medical Imaging.

[4]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[5]  Xiaopeng Wei,et al.  Predicting the Risk of Heart Failure With EHR Sequential Data Modeling , 2018, IEEE Access.

[6]  Tien Yin Wong,et al.  Glaucoma detection based on deep convolutional neural network , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Feng Liu,et al.  Usefulness of frequency-doubling technology for perimetrically normal eyes of open-angle glaucoma patients with unilateral field loss. , 2010, Ophthalmology.

[8]  Robert N Weinreb,et al.  Development and Validation of a Deep Learning System to Detect Glaucomatous Optic Neuropathy Using Fundus Photographs. , 2019, JAMA ophthalmology.

[9]  Mark Hoogendoorn,et al.  Using recurrent neural networks to predict colorectal cancer among patients , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[10]  Sung Wook Baik,et al.  A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction , 2019, Complex..

[11]  Ping Zhang,et al.  Risk Prediction with Electronic Health Records: A Deep Learning Approach , 2016, SDM.

[12]  Mikel Galar,et al.  Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy , 2016, Appl. Soft Comput..

[13]  Suman V. Ravuri,et al.  A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury , 2019, Nature.

[14]  Robert Ritch,et al.  The ISNT rule and differentiation of normal from glaucomatous eyes. , 2006, Archives of ophthalmology.

[15]  T. Wong,et al.  Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. , 2014, Ophthalmology.

[16]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).