Weakly Supervised Vitiligo Segmentation in Skin Image through Saliency Propagation

Abstract

Vitiligo is a skin disorder where pale or white patches develop due to the lack or absence of melanocytes. Vitiligo affects around 0.5% to 1% of the world's population, and it may have a profound psychological impact on patients' quality of life. In this paper, we present a novel weakly supervised framework to segment vitiligo regions with high quality, which is a fundamental task for the assessment of vitiligo. The proposed framework starts with pre-training a classification network using only image-level labels. Then we observed that the activation map obtained from the image classification network could be further exploited and introduced into the saliency propagation process as useful information. Finally, the saliency propagation process is performed on the graph built on superpixels to obtain a meaningful saliency map. These three steps lead to a compelling yet elegant method. Moreover, we propose a new large vitiligo image dataset named Vit2019. To the best of our knowledge, this is currently the first dataset for image segmentation of vitiligo diseases. Experimental results demonstrate the superiority of the proposed model over state-of-the-arts.