论文标题
学习组织学图像分类和检索的二进制语义嵌入
Learning Binary Semantic Embedding for Histology Image Classification and Retrieval
论文作者
论文摘要
随着医学成像技术和机器学习的发展，可以为病理学家提供令人印象深刻的参考的计算机辅助诊断，吸引了广泛的研究兴趣。医学图像的指数增长和传统分类模型的无法解释性阻碍了计算机辅助诊断的应用。为了解决这些问题，我们提出了一种学习二进制语义嵌入（LBSE）的新方法。根据有效且有效的嵌入，进行分类和检索，以提供可解释的组织学图像诊断。此外，双重监督，位不相关和平衡约束，不对称策略和离散优化被无缝整合到拟议的学习二进制嵌入方法中。在三个基准数据集上进行的实验验证了各种情况下LBSE的优势。
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.
