Search

Content-based histopathology image retrieval can be a useful technique for help in diagnosing various diseases. The process of retrieving images is often time-consuming and challenging due to the need for high-dimensional features when trying to model complex content. Hashing methods can therefore be employed to resolve the challenge by producing binary codes of different lengths. Deep hashing methods are frequently superior to traditional machine learning approaches but are affected by the size of training sets. In addition, back-propagation learning can further complicate the generation of binary values. Hence, this paper proposes a novel Siamese deep hashing model, named histopathology Siamese deep hashing (HSDH), for histopathology image retrieval. Two designed deep hashing models with shared weights and structures are used to generate hash codes. A Hamming distance layer is then applied to evaluate the similarity of the generated values. A highly effective loss function is also introduced that incorporates a modified version of the standard contrastive loss function with an error estimation term to improve both the training and retrieval phases. In the retrieval phase, the trained model compares a query image with all the training images and ranks the most similar images. According to the experimental results on two publicly available databases, BreakHis and Kather, the HSDH model outperforms other state-of-the-art hashing-based methods in histopathology image retrieval. [ABSTRACT FROM AUTHOR]

• To learn compact codes, we force mapping matrix of each view to orthogonalize. • A binary graph structure is embedded into one-step binary multi-view clustering. • An effective solving algorithm based on ADM is proposed for the above model. • Empirical experimental results illustrate that the effectiveness of the method. The multi-view clustering problem has attracted considerable attention over recent years for the remarkable clustering performance due to exploiting complementary information from multiple views. Most existing related research work processes data in the decimal real value space that is not the most compatible space for computers. Binary code learning, also known as hashing technology, is well-known for fast Hamming distance computation, less storage requirement and accurate calculation results. The Hamming space is most enjoyed by computers because of binary/hash codes. Several studies combine multi-view clustering with binary code learning for improving clustering performance. However, there is much redundant information contained in the learned binary codes, which negatively affects the clustering performance, but these studies ignore eliminating redundant information for learning compact codes. In addition, they don't give a unified (one-step) clustering framework with binary graph structure, which doesn't lead to the optimal clustering result due to the information loss during the two-step process. In this paper, to cope with the two issues, we propose an orthogonal mapping binary graph method (OMBG) for the multi-view clustering problem, which makes the mapping matrix of every view orthogonalize for eliminating redundant information and embeds a binary graph structure into the unified binary multi-view clustering framework for extracting local geometric structure information of binary codes and achieving the optimal clustering result. Furthermore, we design an effective optimization algorithm based on alternating direction minimization to solve the model of OMBG. Extensive experiments performed on four frequently-used benchmark multi-view datasets illustrate the superiority of OMBG which is compared with some state-of-the-art clustering baselines. [ABSTRACT FROM AUTHOR]