DenseHashNet: A Novel Deep Hashing for Medical Image Retrieval
Chuansheng Liu, Weiping Ding, Chun Chor Litwin Cheng, Cheng Chun Tang, Jiashuang Huang, Haipeng Wang · IEEE Journal of Radio Frequency Identification · 2022
With the wide application of imaging modalities such as X-ray and Computed Tomography (CT) in clinical practice, Content-based Medical Image Retrieval (CBMIR) has become a current research hotspot. Related studies have shown that hash-based image retrieval algorithms can retrieve relevant images faster and more accurately than traditional image retrieval methods. Therefore, in this paper, we propose a novel deep hashing method for medical image retrieval, called DenseHashNet. Specifically, we first use DenseNet to extract the original image features, and introduce the Spatial Pyramid Pooling (SPP) layer after the last Dense Block so that features at different scales can be extracted and multi-scale features fused with information from multiple regions. Then, the output of the SPP layer is subjected to Power-Mean Transformation (PMT) operation to enhance the nonlinearity of the model and improve the performance of the model. Finally, we map the output of PMT to hash codes through fully connected layers. Experimental results show that our method achieves better performance, compared with some representative methods.