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.

Machine Learning Techniques for Automated Melanoma Detection The malignant melanoma is one of the most aggressive forms of skin cancer. Modern Dermatology recognizes early diagnosis as a fundamental role in reducing the mortality rate and to guarantee less invasive treatments for patients. ComputerAided Diagnosis (CAD) systems are increasingly adopted for the early diagnosis of skin lesions. These systems consist of different phases that must be chosen appropriately based on the characteristics of digital images aiming to obtain a reliable diagnosis. Acquisition, preprocessing, segmentation, feature extraction and selection, and finally classification of dermoscopic images hold challenges to be faced and overcome to improve the automatic diagnosis of dangerous lesions such as melanoma. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the various machine learning approaches that have been proposed and that provide inspiration for the implementation of effective frameworks.