Multi-task Fundus Image Quality Assessment via Transfer Learning and Landmarks Detection

  • Yaxin Shen
  • Ruogu FangEmail author
  • Bin ShengEmail author
  • Ling Dai
  • Huating Li
  • Jing Qin
  • Qiang Wu
  • Weiping Jia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)


The quality of fundus images is critical for diabetic retinopathy diagnosis. The evaluation of fundus image quality can be affected by several factors, including image artifact, clarity, and field definition. In this paper, we propose a multi-task deep learning framework for automated assessment of fundus image quality. The network can classify whether an image is gradable, together with interpretable information about quality factors. The proposed method uses images in both rectangular and polar coordinates, and fine-tunes the network from trained model grading of diabetic retinopathy. The detection of optic disk and fovea assists learning the field definition task through coarse-to-fine feature encoding. The experimental results demonstrate that our framework outperform single-task convolutional neural networks and reject ungradable images in automated diabetic retinopathy diagnostic systems.


Fundus image quality assessment Multi-task learning Optic disk detection Fovea detection 



This work is partially supported by National Key Research and Development Program of China (No: 2016YFC1300302, 2017YFE0104000) and by National Natural Science Foundation of China (No: 61525106, 61427807).

Supplementary material

473959_1_En_4_MOESM1_ESM.pdf (757 kb)
Supplementary material 1 (pdf 756 KB)


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Shanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
  4. 4.School of NursingThe Hong Kong Polytechnic UniversityHong KongChina

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