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

Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval