Detect glaucoma with image segmentation and transfer learning

Lianyi Wu, Yiming Liu, Yelin Shi, Bin Sheng, Ping Li, Lei Bi, Jinman Kim

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In this paper, we aim to automatically detect glaucoma via deep learning. To do that, we need to calculate the cup-to-disc ratio (CDR) on fine segmented retina images. To get precise segmentation, we implemented SegNet together with adversarial discriminative domain adaptation (ADDA), the former is a famous artificial neural network with encoder-decoder architecture used in image segmentation area and the latter is a transfer learning method for domain adaptation. We are the first to combine them together to detect glaucoma on test dataset which have different brightness from our training dataset. We thoroughly evaluated the proposed method with various loss functions, normal cross entropy loss, weighted cross entropy loss and dice coefficient loss included. And we show that dice loss is the best for this task. Last but not least, our experiments on transfer learning have shown that our ADDA method reduces the mean square error (MSE) between the CDR of our segmentation and annotations greatly.

Original languageEnglish
Title of host publicationProceedings of the 32nd International Conference on Computer Animation and Social Agents, CASA 2019
PublisherAssociation for Computing Machinery
Pages37-40
Number of pages4
ISBN (Electronic)9781450371599
DOIs
Publication statusPublished - 1 Jul 2019
Externally publishedYes
Event32nd International Conference on Computer Animation and Social Agents, CASA 2019 - Paris, France
Duration: 1 Jul 20193 Jul 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference32nd International Conference on Computer Animation and Social Agents, CASA 2019
Country/TerritoryFrance
CityParis
Period1/07/193/07/19

Keywords

  • ADDA
  • Glaucoma
  • Segmentation
  • SegNet
  • Transfer learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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