Year: 2020
Type: conference-paper
Abstract: Malignant melanoma is responsible for the highest number of deaths related to skin lesions.The similarities of melanoma with other skin lesions, such as dysplastic nevi, constitute a pitfall for computerized 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.Currently, there is a 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 tools useful for skin lesion detection.
Authors: Eugenio Vocaturo, Ester Zumpano
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3082546546