Dermoscopic image retrieval based on rotation-invariance deep hashing

Abstract Dermoscopic image retrieval technology can provide dermatologists with valuable information such as similar confirmed skin disease cases and their diagnosis reports to assist doctors in their diagnosis. In this study, we design a dermoscopic image retrieval algorithm using convolutional neural networks (CNNs) and hash coding. A hybrid dilated convolution spatial attention module is proposed, which can focus on key information and suppress irrelevant information based on the complex morphological characteristics of dermoscopic images. Furthermore, we also propose a cauchy rotation invariance loss function in view of the skin lesion target without the main direction. This function constrains CNNs to learn output differences in samples from different angles and to make CNNs obtain a certain rotation invariance. Extensive experiments are conducted on a dermoscopic image dataset to verify the effectiveness and versatility of the proposed module, algorithm, and loss function. Experiment results show that the rotation-invariance deep hashing network with proposed spatial attention module obtains better performance on the dermoscopic image retrieval.