Unsupervised deep hashing by joint optimization for pulmonary nodule image retrieval

•Unified feature learning and binary code learning can reduce information loss.•The equal-bit quantization method will accumulate quantization errors.•The feature quantization using relaxation strategy will produce suboptimal solution. In recent years, hash-based image retrieval has attracted great attention due to the rapid growth of medical images. In the paper, an end-to-end unsupervised deep hashing is proposed, where feature extraction and binary optimization are carried out by joint optimization. Our method consists of five components: a shared deep convolution neural network for learning image representations, a deconvolution module for reconstructing the original images, a classification module for leveraging semantic supervision by pseudo labels, a binary code learning module for encoding images features into binary codes, and a joint loss function for deep hash function learning. In addition, the real-valued features balanced in different dimensions by a rotation matrix are quantized directly into discrete binary codes in an alternating optimization approach to minimize the quantization loss. Experiments have been performed on the pulmonary nodule images dataset and the results demonstrate the proposed method can yield better retrieval performance by comparing with the state-of-the-art unsupervised hashing methods.