Retinal Microaneurysm Detection Using Clinical Report Guided Multi-sieving CNN

  • Ling Dai
  • Bin ShengEmail author
  • Qiang Wu
  • Huating LiEmail author
  • Xuhong Hou
  • Weiping Jia
  • Ruogu Fang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Timely detection and treatment of microaneurysms (MA) is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting MAs in fundus images is a highly challenging task due to the large variation of imaging conditions. In this paper, we focus on developing an interleaved deep mining technique to cope intelligently with the unbalanced MA detection problem. Specifically, we present a clinical report guided multi-sieving convolutional neural network (MS-CNN) which leverages a small amount of supervised information in clinical reports to identify the potential MA regions via a text-to-image mapping in the feature space. These potential MA regions are then interleaved with the fundus image information for multi-sieving deep mining in a highly unbalanced classification problem. Critically, the clinical reports are employed to bridge the semantic gap between low-level image features and high-level diagnostic information. Extensive evaluations show our framework achieves 99.7% precision and 87.8% recall, comparing favorably with the state-of-the-art algorithms. Integration of expert domain knowledge and image information demonstrates the feasibility to reduce the training difficulty of the classifiers under extremely unbalanced data distribution.



This work is supported by National High-tech R&D Program of China (863 Program) (2015AA015904), NSFC (61572316, 61671290, 61525106), National Key R&D Program of China (2016YFC1300302), Key Program for International S&T Cooperation Project (2016YFE0129500) of China, Science and Technology Commission of Shanghai Municipality (16DZ0501100), and Interdisciplinary Program of Shanghai Jiao Tong University (14JCY10).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiChina
  3. 3.School of Computing and Information SciencesFlorida International UniversityMiamiUSA

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