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Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images

  • Sebastian Otálora
  • Oscar Perdomo
  • Fabio González
  • Henning Müller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10552)

Abstract

Training deep convolutional neural network for classification in medical tasks is often difficult due to the lack of annotated data samples. Deep convolutional networks (CNN) has been successfully used as an automatic detection tool to support the grading of diabetic retinopathy and macular edema. Nevertheless, the manual annotation of exudates in eye fundus images used to classify the grade of the DR is very time consuming and repetitive for clinical personnel. Active learning algorithms seek to reduce the labeling effort in training machine learning models. This work presents a label-efficient CNN model using the expected gradient length, an active learning algorithm to select the most informative patches and images, converging earlier and to a better local optimum than the usual SGD (Stochastic Gradient Descent) strategy. Our method also generates useful masks for prediction and segments regions of interest.

Notes

Acknowledgments

This work was supported by the Administrative Department of Science, Technology and Innovation of Colombia (Colciencias) through the grant Jóvenes Investigadores 2014 in call 645 and by Nvidia with a TitanX GPU.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sebastian Otálora
    • 2
  • Oscar Perdomo
    • 1
  • Fabio González
    • 1
  • Henning Müller
    • 2
  1. 1.Universidad Nacional de ColombiaBogotáColombia
  2. 2.University of Applied Sciences Western Switzerland (HES-SO)SierreSwitzerland

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