  Deep hashing methods have been proved to be effective for the large-scale
medical image search assisting reference-based diagnosis for clinicians.
However, when the salient region plays a maximal discriminative role in
ophthalmic image, existing deep hashing methods do not fully exploit the
learning ability of the deep network to capture the features of salient regions
pointedly. The different grades or classes of ophthalmic images may be share
similar overall performance but have subtle differences that can be
differentiated by mining salient regions. To address this issue, we propose a
novel end-to-end network, named Attention-based Saliency Hashing (ASH), for
learning compact hash-code to represent ophthalmic images. ASH embeds a
spatial-attention module to focus more on the representation of salient regions
and highlights their essential role in differentiating ophthalmic images.
Benefiting from the spatial-attention module, the information of salient
regions can be mapped into the hash-code for similarity calculation. In the
training stage, we input the image pairs to share the weights of the network,
and a pairwise loss is designed to maximize the discriminability of the
hash-code. In the retrieval stage, ASH obtains the hash-code by inputting an
image with an end-to-end manner, then the hash-code is used to similarity
calculation to return the most similar images. Extensive experiments on two
different modalities of ophthalmic image datasets demonstrate that the proposed
ASH can further improve the retrieval performance compared to the
state-of-the-art deep hashing methods due to the huge contributions of the
spatial-attention module.
