  Deep learning has been successfully applied to a variety of image
classification tasks. There has been keen interest to apply deep learning in
the medical domain, particularly specialties that heavily utilize imaging, such
as ophthalmology. One issue that may hinder application of deep learning to the
medical domain is the vast amount of data necessary to train deep neural
networks (DNNs). Because of regulatory and privacy issues associated with
medicine, and the generally proprietary nature of data in medical domains,
obtaining large datasets to train DNNs is a challenge, particularly in the
ophthalmology domain.
  Transfer learning is a technique developed to address the issue of applying
DNNs for domains with limited data. Prior reports on transfer learning have
examined custom networks to fully train or used a particular DNN for transfer
learning. However, to the best of my knowledge, no work has systematically
examined a suite of DNNs for transfer learning for classification of diabetic
retinopathy, diabetic macular edema, and two key features of age-related
macular degeneration. This work attempts to investigate transfer learning for
classification of these ophthalmic conditions. Part I gives a condensed
overview of neural networks and the DNNs under evaluation. Part II gives the
reader the necessary background concerning diabetic retinopathy and prior work
on classification using retinal fundus photographs. The methodology and results
of transfer learning for diabetic retinopathy classification are presented,
showing that transfer learning towards this domain is feasible, with promising
accuracy. Part III gives an overview of diabetic macular edema, choroidal
neovascularization and drusen (features associated with age-related macular
degeneration), and presents results for transfer learning evaluation using
optical coherence tomography to classify these entities.
