Multi-level Relationship Capture Network for Automated Skin Lesion Recognition

Automated skin lesion recognition of dermoscopy images is effective for improving diagnostic performance. Current popular solutions either leverage a single image to learn better feature representations or take advantage of pairwise images for more discriminative recognition. However, they ignore modeling the relationship between important regions within the central lesion area, or mining the deeper semantic correlation between different images. In this paper, we propose a novel Multi-level Relationship Capture Network (MRCN), which focuses on relationship mining at two different levels, the region level and the image level. Specifically, a region-correlation learning module is proposed to model the relationship between different important regions in the central lesion area. Meanwhile, a cross-image learning module is designed to model the deep semantic correlation between multiple images. Besides, a lesion discerning module and a consistency regularization module are adopted to extract the feature of the lesion area and to serve as an extra consistency constraint, respectively. Comprehensive experiments are conducted on three challenging datasets, and the experimental results show that our MRCN can achieve the state-of-the-art performance compared to previous work, which demonstrates its advantages and superiority.