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A Content-Based Image Retrieval (CBIR) system which identifies similarmedical images based on a query image can assist clinicians for more accuratediagnosis. The recent CBIR research trend favors the construction and use ofbinary codes to represent images. Deep architectures could learn the non-linearrelationship among image pixels adaptively, allowing the automatic learning ofhigh-level features from raw pixels. However, most of them require classlabels, which are expensive to obtain, particularly for medical images. Themethods which do not need class labels utilize a deep autoencoder for binaryhashing, but the code construction involves a specific training algorithm andan ad-hoc regularization technique. In this study, we explored using a deepde-noising autoencoder (DDA), with a new unsupervised training scheme usingonly backpropagation and dropout, to hash images into binary codes. Weconducted experiments on more than 14,000 x-ray images. By using class labelsonly for evaluating the retrieval results, we constructed a 16-bit DDA and a512-bit DDA independently. Comparing to other unsupervised methods, wesucceeded to obtain the lowest total error by using the 512-bit codes forretrieval via exhaustive search, and speed up 9.27 times with the use of the16-bit codes while keeping a comparable total error. We found that our newtraining scheme could reduce the total retrieval error significantly by 21.9%.To further boost the image retrieval performance, we developed RadonAutoencoder Barcode (RABC) which are learned from the Radon projections ofimages using a de-noising autoencoder. Experimental results demonstrated itssuperior performance in retrieval when it was combined with DDA binary codes.

Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders