Informative Retrieval Framework for Histopathology Whole Slides Images Based on Deep Hashing Network

Histopathology image retrieval is an emerging application for Computer-aided cancer diagnosis. However, the current retrieval methods, especially the methods based on deep hashing, pay less attention to the characteristic of histopathology whole slide images (WSIs). The retrieved results are occasionally occupied by similar images from a few WSIs. The retrieval database cannot be sufficiently utilized. To solve these issues, we proposed an informative retrieval framework based on deep hashing network. Specifically, a novel loss function for the hashing network and a retrieval strategy are designed, which contributes more informative retrieval results without reducing the retrieval precision. The proposed method was verified on the ACDC-LungHP dataset and compared with the state-of-the-art method. The experimental results have demonstrated the effectiveness of our method in the retrieval of large-scale database containing histopathology while slide images.