Abstract
The number of people suffering from retinal diseases increases with population aging and the popularity of electronic screens. Previous studies on deep learning based automatic screening generally focused on specific types of retinal diseases, such as diabetic retinopathy and glaucoma. Since patients may suffer from various types of retinal diseases simultaneously, these solutions are not clinically practical. To address this issue, we propose a novel deep learning based method that can recognise 36 different retinal diseases with a single model. More specifically, the proposed method uses a region-specific multi-task recognition model by learning diseases affecting different regions of the retina with three sub-networks. The three sub-networks are semantically trained to recognise diseases affecting optic-disc, macula and entire retina. Our contribution is two-fold. First, we use multitask learning for retinal disease classification and achieve significant improvements for recognising three main groups of retinal diseases in general, macular and optic-disc regions. Second, we collect a multi-label retinal dataset to the community as standard benchmark and release it for further research opportunities.
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Notes
- 1.
- 2.
We experimented with all Inception-V3/Inception-Resnet-V2 settings and figured out a mixture of them gave the best performance of all. Network training used Adam with a learning rate of 1e-5 which decayed every three epochs with ratio of 0.9 for total 14 epochs. We used Keras distributed machine learning system with 8 replicas running each on a NVidia 1080Ti GPU. The input size of general stream is 800, while the input size of macular and optic-task stream is 600 and 300 respectively.
- 3.
We set a label named as other general, macular or optic-disc disease to indicate a gathering of rare diseases in each task. This label for optic-disc task represents disease such as morning glory syndrome, melanocytoma of optic disc, membrane tissue on the optic disc and etc.
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Wang, X., Ju, L., Zhao, X., Ge, Z. (2019). Retinal Abnormalities Recognition Using Regional Multitask Learning. 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_4
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