  Deep hashing methods have been shown to be the most efficient approximate
nearest neighbor search techniques for large-scale image retrieval. However,
existing deep hashing methods have a poor small-sample ranking performance for
case-based medical image retrieval. The top-ranked images in the returned query
results may be as a different class than the query image. This ranking problem
is caused by classification, regions of interest (ROI), and small-sample
information loss in the hashing space. To address the ranking problem, we
propose an end-to-end framework, called Attention-based Triplet Hashing (ATH)
network, to learn low-dimensional hash codes that preserve the classification,
ROI, and small-sample information. We embed a spatial-attention module into the
network structure of our ATH to focus on ROI information. The spatial-attention
module aggregates the spatial information of feature maps by utilizing
max-pooling, element-wise maximum, and element-wise mean operations jointly
along the channel axis. The triplet cross-entropy loss can help to map the
classification information of images and similarity between images into the
hash codes. Extensive experiments on two case-based medical datasets
demonstrate that our proposed ATH can further improve the retrieval performance
compared to the state-of-the-art deep hashing methods and boost the ranking
performance for small samples. Compared to the other loss methods, the triplet
cross-entropy loss can enhance the classification performance and hash
code-discriminability
