Abstract:
We propose a novel multiple-instance learning
(MIL) 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 into 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 an RNFL data set containing 884 images
annotatedby two ophthalmologistsgive a system-annotator
agreement (kappa values) of 0.73 and 0.72, respectively,
with an interannotator agreement of 0.73. Our system
agrees better with the more experienced annotator. Comparative
tests with three public data sets (MESSIDOR and
DR for diabetic retinopathy, and 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 https://github.com/ManiShiyam/Sub-categoryclassifiers-
for-Multiple-Instance-Learning/wiki.
