Weakly Supervised Vitiligo Segmentation in Skin Image through Saliency Propagation 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 worldu0027s population, and it may have a profound psychological impact on patientsu0027 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 pretraining a classification network using only imagelevel 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 stateofthearts.

Dangerousness of dysplastic nevi: a Multiple Instance Learning Solution for Early Diagnosis Malignant melanoma is responsible for the highest number of deaths related to skin lesions. However, early diagnosis may allow positive treatment of this terrible form of cancer. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. The proposed algorithms and methods have had as main focus the dichotomous distinction of melanoma from benign lesions and they rarely focused on the case of melanoma against dysplastic nevi. This challenge is much more difficult due to the similarity of the injuries. Currently, there is debate about dysplastic nevi syndrome, or rather about the number of moles present on the human body as potential melanoma risk factors. In this document, we consider the challenging task of applying a multiinstance learning (MIL) algorithm for discriminating melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common nevi. Since the results appear promising, we conclude that a MIL technique could be at the basis of more sophisticated tools useful to skin lesion detection.