  With the development of medical imaging technology and machine learning,
computer-assisted diagnosis which can provide impressive reference to
pathologists, attracts extensive research interests. The exponential growth of
medical images and uninterpretability of traditional classification models have
hindered the applications of computer-assisted diagnosis. To address these
issues, we propose a novel method for Learning Binary Semantic Embedding
(LBSE). Based on the efficient and effective embedding, classification and
retrieval are performed to provide interpretable computer-assisted diagnosis
for histology images. Furthermore, double supervision, bit uncorrelation and
balance constraint, asymmetric strategy and discrete optimization are
seamlessly integrated in the proposed method for learning binary embedding.
Experiments conducted on three benchmark datasets validate the superiority of
LBSE under various scenarios.
