期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 96, 期 -, 页码 93-106出版社
ELSEVIER
DOI: 10.1016/j.artmed.2019.03.009
关键词
Diabetic retinopathy; Referral; Screening; Multi-resolution training; Robust feature-extraction augmentation; Integrated patient-basis analysis
资金
- Kaggle
- California Healthcare Foundation
- EyePACs
- NVIDIA Corporation
- Microsoft Research
- Sao Paulo Research Foundation (Fapesp) [2017/12646-3]
- National Council for Scientific Research (CNPq) [311486/2014-2, 304472/2015-8]
- Amazon Web Services
- CAPES DeepEyes project
- CAPES/PNPD
- Google Research
Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize. Objective: We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector. Methods: We gradually build the solution based on convolutional neural networks, adding data augmentation, multi-resolution training, robust feature-extraction augmentation, and a patient-basis analysis, testing the effectiveness of each improvement. Results: The proposed method achieved an area under the ROC curve of 98.2% (95% CI: 97.4-98.9%) under a strict cross-dataset protocol designed to test the ability to generalize - training on the Kaggle competition dataset and testing using the Messidor-2 dataset, With a 5 x 2-fold cross-validation protocol, similar results are achieved for Messidor-2 and DR2 datasets, reducing the classification error by over 44% when compared to most published studies in existing literature. Conclusion: Additional boost strategies can improve performance substantially, but it is important to evaluate whether the additional (computation- and implementation-) complexity of each improvement is worth its benefits. We also corroborate that novel families of data-driven methods are the state of the art for diabetic retinopathy screening. Significance: By learning powerful discriminative patterns directly from available training retinal images, it is possible to perform referral diagnostics without detecting individual lesions.
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