Learning Binary Semantic Embedding for Large-Scale Breast Histology Image Analysis
Xingbo Liu, Xiao Kang, Xiushan Nie, Jie Guo, Shaohua Wang, Yilong Yin · IEEE Journal of Biomedical and Health Informatics · 2022
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 uncorrela-tion 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.