Retinal Abnormalities Recognition Using Regional Multitask Learning

  • Xin Wang
  • Lie Ju
  • Xin Zhao
  • Zongyuan GeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11764)


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.

Supplementary material

486666_1_En_4_MOESM1_ESM.pdf (8.2 mb)
Supplementary material 1 (pdf 8399 KB)


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Airdoc LLCBeijingChina
  2. 2.Monash eResearch CenterMonash UniversityClaytonAustralia

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