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 multi-instance 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.