Automatic Detection of Dysplastic Nevi: A Multiple Instance Learning Solution. Vocaturo, E. & Zumpano, E. 2020. cited By 0
Automatic Detection of Dysplastic Nevi: A Multiple Instance Learning Solution. [link]Paper  abstract   bibtex   
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 fo-cus 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 chal-lenge 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. Copyright © 2020 for this paper by its authors.
@CONFERENCE{Vocaturo2020250,
author={Vocaturo, E. and Zumpano, E.},
title={Automatic Detection of Dysplastic Nevi: A Multiple Instance Learning Solution.},
journal={CEUR Workshop Proceedings},
year={2020},
volume={2646},
pages={250-257},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090874250&partnerID=40&md5=b1ba2cff5cf63e08a7857fa2f9360ed5},
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 fo-cus 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 chal-lenge 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. Copyright © 2020 for this paper by its authors.},
editor={Agosti M., Atzori M., Ciaccia P., Tanca L.},
publisher={CEUR-WS},
issn={16130073},
document_type={Conference Paper},
source={Scopus},
}

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