Pairwise based deep ranking hashing for histopathology image classification and retrieval

Hashing has become a popular tool on histopathology image analysis due to the significant gain in both computation and storage. However, most of current hashing techniques learn features and binary codes individually from whole images, or emphasize the inter-class difference but neglect the relevance order within the same classes. To alleviate these issues, in this paper, we propose a novel pairwise based deep ranking hashing framework. We first define a pairwise matrix to preserve intra-class relevance and inter-class difference. Then we propose an objective function that utilizes two identical continuous matrices generated by the hyperbolic tangent (tanh) function to approximate the pairwise matrix. Finally, we incorporate the objective function into a deep learning architecture to learn features and binary codes simultaneously. The proposed framework is validated on 5356 skeletal muscle and 2176 lung cancer images with four types of diseases, and it can achieve 97.49% classification accuracy, 97.49% mean average precision (MAP) with 100 returned images, and 0.51 NDCG score with 50 retrieved neighbors on 2032 query images.