Deep Triplet Hashing Network for Case-based Medical Image Retrieval

• URL: http://arxiv.org/abs/2101.12346v1
• Date: Fri, 29 Jan 2021 01:35:46 GMT
• Title: Deep Triplet Hashing Network for Case-based Medical Image Retrieval
• Authors: Jiansheng Fang, Huazhu Fu, Jiang Liu
• Abstract summary: We propose an end-to-end framework, called Attention-based Triplet Hashing (ATH) network, to learn low-dimensional hash codes. We show that our proposed ATH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods.
• Score: 33.21919320742157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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