LAUSR

With the progress of clinical imaging innovation and machine learning, the computer-assisted diagnosis of breast histology images has attracted broad attention. Nonetheless, the use of computer-assisted diagnoses has been blocked due to the incomprehensibility of customary classification models. In view of this question, we propose a novel method for Learning Binary Semantic Embedding (LBSE). In this study, bit balance and uncorrelation constraints, double supervision, discrete optimization and asymmetric pairwise similarity are seamlessly integrated for learning binary semantic-preserving embedding. Moreover, a fusion-based strategy is carefully designed to handle the intractable problem of parameter setting, saving huge amounts of time for boundary tuning. Based on the above-mentioned proficient and effective embedding, classification and retrieval are simultaneously performed to give interpretable image-based deduction and model helped conclusions for breast histology images. Extensive experiments are conducted on three benchmark datasets to approve the predominance of LBSE in different situations.