Order-Sensitive Deep Hashing for Multimorbidity Medical Image Retrieval

In this paper, we propose an order-sensitive deep hashing for scalable medical image retrieval in the scenario of coexistence of multiple medical conditions. The pairwise similarity preservation in existing hashing methods is not suitable for this multimorbidity medical image retrieval problem. To capture the multilevel semantic similarity, we formulate it as a multi-label hashing learning problem. We design a deep hash model for powerful feature extraction and preserve the ranking list with a triplet based ranking loss for better assessment assistance. We further introduce the cross-entropy based multi-label classification loss to exploit multi-label information. We solve the optimization problem by continuation to reduce the quantization loss. We conduct extensive experiments on a large database constructed on the NIH Chest X-ray database to validate the efficacy of the proposed algorithm. Experimental results demonstrate that our order sensitive deep hashing leads to superior performance compared with several state-of-the-art hashing methods.