  Deep neural networks have been investigated in learning latent
representations of medical images, yet most of the studies limit their approach
in a single supervised convolutional neural network (CNN), which usually rely
heavily on a large scale annotated dataset for training. To learn image
representations with less supervision involved, we propose a deep Siamese CNN
(SCNN) architecture that can be trained with only binary image pair
information. We evaluated the learned image representations on a task of
content-based medical image retrieval using a publicly available multiclass
diabetic retinopathy fundus image dataset. The experimental results show that
our proposed deep SCNN is comparable to the state-of-the-art single supervised
CNN, and requires much less supervision for training.
