Joint Kernel-Based Supervised Hashing for Scalable Histopathological Image Analysis

Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Recently, with the rapid growth of histopathological images, hashing-based retrieval approaches are gaining popularity due to their exceptional scalability. In this paper, we exploit a joint kernel-based supervised hashing (JKSH) framework for fusion of complementary features. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features, and supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on thousands of breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.2% classification accuracy within 14.0 ms query time, comparing favorably with traditional methods.