Deep Learning Features for Modeling Perceptual Similarity in Microcalcification Lesion Retrieval

Retrieving cases with similar image features has been found to be effective for improving the diagnostic accuracy of microcalcification (MC) lesions in mammograms. However, a major challenge in such an image-retrieval approach is how to determine a retrieved lesion image has diagnostically similar features to that of a query case. We investigate the feasibility of modeling perceptually similar MC lesions by using deep learning features extracted from two types of deep neural networks, of which one is a supervised-learning network developed for the task of MC detection and the other is a denoising autoencoder network. In the experiments, the deep learning features were compared against the perceptual similarity scores collected from a reader study on 1,000 MC lesion image pairs. The results indicate that the deep learning features can potentially be more effective for modelling the notion of perceptual similarity of MC lesions than traditional handcrafted texture features.