Hashing-based medical image retrieval has drawn extensive attention recently, which aims at providing effective aided diagnosis for medical personnel. In the paper, a novel deep hashing framework is proposed in the medical image retrieval, where the processes of deep feature extraction, binary code learning, and deep hash function learning are jointly carried out in supervised fashion. Particularly, the discrete constrained objective function in the hash code learning is optimized iteratively, where the binary code can be directly solved with no need for relaxation. In the meantime, the semantic similarity is maintained by fully exploring supervision information during the discrete optimization, where the neighborhood structure of training data is preserved by applying a graph regularization term. Additionally, to gain the fine-grained ranking of the returned medical images sharing the same Hamming distance, a novel image re-ranking scheme is proposed to refine the similarity measurement by jointly considering Euclidean distance between the real-valued feature descriptors and their category information between those images. Extensive experiments on the pulmonary nodule image dataset demonstrate that the proposed method can achieve better retrieval performance over the state of the arts.

基于散列的医学图像检索近来引起了广泛关注，其目的是为医务人员提供有效的辅助诊断。本文在医学图像检索中提出了一种新颖的深度哈希框架，该框架以监督的方式联合进行深度特征提取，二进制代码学习和深度哈希函数学习的过程。特别地，哈希码学习中的离散约束目标函数被迭代地优化，其中二进制码可以直接求解而无需放松。同时，通过在离散优化过程中充分探索监督信息来保持语义相似性，其中通过应用图正则化项来保留训练数据的邻域结构。另外，为了获得共享相同汉明距离的返回医学图像的细粒度排名，提出了一种新的图像重新排名方案，通过共同考虑实值特征描述符与其之间的类别信息之间的欧式距离来细化相似性度量图片。在肺结节图像数据集上的大量实验表明，所提出的方法可以在现有技术水平上实现更好的检索性能。