  According to the World Health Organization, 285 million people worldwide live
with visual impairment. The most commonly used imaging technique for diagnosis
in ophthalmology is optical coherence tomography (OCT). However, analysis of
retinal OCT requires trained ophthalmologists and time, making a comprehensive
early diagnosis unlikely. A recent study established a diagnostic tool based on
convolutional neural networks (CNN), which was trained on a large database of
retinal OCT images. The performance of the tool in classifying retinal
conditions was on par to that of trained medical experts. However, the training
of these networks is based on an enormous amount of labeled data, which is
expensive and difficult to obtain. Therefore, this paper describes a method
based on variational autoencoder regularization that improves classification
performance when using a limited amount of labeled data. This work uses a
two-path CNN model combining a classification network with an autoencoder (AE)
for regularization. The key idea behind this is to prevent overfitting when
using a limited training dataset size with small number of patients. Results
show superior classification performance compared to a pre-trained and fully
fine-tuned baseline ResNet-34. Clustering of the latent space in relation to
the disease class is distinct. Neural networks for disease classification on
OCTs can benefit from regularization using variational autoencoders when
trained with limited amount of patient data. Especially in the medical imaging
domain, data annotated by experts is expensive to obtain.
