A multiple instance learning approach for the automatic classification of skin lesions

The number of deaths linked to skin cancers has malignant melanoma as the main culprit. Early diagnosis helps manage this terrible form of cancer, but the similarity of melanoma to other skin lesions is an obstacle to effective detection. The scientific community is proposing different solutions to support the computerized analysis of skin lesions mainly focused on the dichotomous distinction of melanoma from benign lesions. The dysplastic nevi syndrome (DNS) correlates the number of moles present in the human body with an increased risk of melanoma development. Nowadays, the classification task concerning the differentiation of dysplastic nevi from common ones is still very little explored. In this paper, we explore the possibility of applying multiple instance learning (MIL) approaches to discriminate melanoma from dysplastic nevi and outline the even more complex challenge of discriminate between dysplastic and common nevi. The obtained results confirm that MIL techniques are useful for the automatic detection of skin lesions are promising, and give hope MIL techniques can be useful for solutions aiming at automatic detection of skin lesions.