A data-driven approach to referable diabetic retinopathy detection.

Journal: Artificial intelligence in medicine
Published Date: Mar 27, 2019

Abstract

UNLABELLED: Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize.

Authors

  • Ramon Pires
    Institute of Computing, University of Campinas (Unicamp), Campinas 13083-852, Brazil. Electronic address: pires.ramon@ic.unicamp.br.
  • Sandra Avila
    School of Electrical and Computing Engineering, University of Campinas, Brazil. Electronic address: sandra@dca.fee.unicamp.br.
  • Jacques Wainer
    Institute of Computing, University of Campinas (Unicamp), Campinas 13083-852, Brazil. Electronic address: wainer@ic.unicamp.br.
  • Eduardo Valle
    School of Electrical and Computing Engineering, University of Campinas, Brazil.
  • Michael D Abramoff
    Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA.
  • Anderson Rocha
    Institute of Computing, University of Campinas, Brazil. Electronic address: anderson.rocha@ic.unicamp.br.

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