Supporting the diagnosis of Dysplastic Nevi Syndrome via Multiple Instance Learning approaches

Malignant melanoma is the form responsible for the greatest number of deaths among skin cancers. The possibility of ensuring survival passes through an early diagnosis and subsequent skin excision. One of the problems that most hinders early diagno- sis, conducted both with the naked eye and through dedicated frame- works, is the extreme similarity of melanoma with other skin lesions such as dysplastic nevi. The possibility of intercepting recurring pat- terns through increasingly advanced diagnostic tools pushes the re- search community to propose software solutions that favor the detec- tion of melanoma. Currently the existing solutions are typically con- centrated in the binary discrimination of melanoma from common nevi. The high presence of common and atypical nevi on the body surface constitutes a potential risk factor for the onset of melanoma and characterizes the current debate on Dysplastic Nevi Syndrome (DNS). The presence of dysplastic nevi complicates the classifica- tion of melanoma from benign nevi, and raises a new classification problem relating to the distinction between dysplastic and common nevi, mostly unexplored. Over time, several machine learning algo- rithms have been proposed to support the image classification phase. In this article, we highlight multiple-instance learning approaches to discriminate melanoma from dysplastic nevi and to address the new challenge of classifying dysplastic from common nevi.