Sub-Category Classifiers for Multiple-Instance Learning and its Application to Retinal Nerve Fiber Layer Visibility Classification

Siyamalan Manivannan (Lead / Corresponding author), Caroline Cobb, Stephen Burgess, Emanuele Trucco

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We propose a novel multiple instance learning method to assess the visibility (visible/not visible) of the retinal nerve fiber layer (RNFL) in fundus camera images. Using only image-level labels, our approach learns to classify the images as well as to localize the RNFL visible regions. We transform
the original feature space to a discriminative subspace, and learn a region-level classifier in that subspace. We propose a margin-based loss function to jointly learn this subspace and the region-level classifier. Experiments with a RNFL dataset containing 884 images annotated by two ophthalmologists give
a system-annotator agreement (kappa values) of 0.73 and 0.72 respectively, with an inter-annotator agreement of 0.73. Our system agrees better with the more experienced annotator. Comparative tests with three public datasets (MESSIDOR and DR for diabetic retinopathy, UCSB for breast cancer)
show that our novel MIL approach improves performance over the state-of-the-art. Our Matlab code is publicly available at
Original languageEnglish
Pages (from-to)1140-1150
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number5
Early online date16 Jan 2017
Publication statusPublished - May 2017



  • multiple instance learning
  • image classification
  • retinal nerve fiber layer
  • retinal image processing
  • retinal biomarkers for dementia

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