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

11/22/2017
by   Yu-An Chung, et al.
0

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) 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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset