Multi-level magnification correlation hashing for scalable histopathological image retrieval

Abstract

Content-based Medical Image Retrieval (CBMIR) presents promising results in computer-aided diagnosis which provides the clinicians with interpretative guidance based on the visual similarity. In this paper, we focus on the tasks of histopathological image retrieval for breast cancer diagnosis. Instead of the holistic-based and cell-based methods proposed in previous works, we propose a multi-magnification correlation hashing (MMCH) framework that learns the discriminative binary codes for histopathological images. The patch-link graph is constructed with both low and high magnification images where the semantic links are propagated from the pre-labeled patches to unlabeled data. We then learn the binary hashing codes based on the global labels from the low-magnification images with structure-regularization. The experiments on BreakHis dataset and PLOSONE dataset demonstrate that MMCH outperforms the previous hashing methods on histopathological image retrieval.