An Innovative Attention-based Triplet Deep Hashing Approach to Retrieve Histopathology Images

Content-based histopathology image retrieval (CBHIR) can assist in the diagnosis of different diseases. The retrieval procedure can be complex and time-consuming if high-dimensional features are required. Thus, hashing techniques are employed to address these issues by mapping the feature space into binary values of varying lengths. The performance of deep hashing approaches in image retrieval is often superior to that of traditional hashing methods. Among deep hashing approaches, triplet-based models are typically more effective than pairwise ones. Recent studies have demonstrated that incorporating the attention mechanism into a deep hashing approach can improve its effectiveness in retrieving images. This paper presents an innovative triplet deep hashing strategy based on the attention mechanism for retrieving histopathology images, called histopathology attention triplet deep hashing (HATDH). Three deep attention-based hashing models with identical architectures and weights are employed to produce binary values. The proposed attention module can aid the models in extracting features more efficiently. Moreover, we introduce an improved triplet loss function considering pair inputs separately in addition to triplet inputs for increasing efficiency during the training and retrieval steps. Based on experiments conducted on two public histopathology datasets, BreakHis and Kather, HATDH significantly outperforms state-of-the-art hashing algorithms.