DEEP LEARNING BASED UV FACIAL IMAGING GENERATION

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Arquivos
art_NAVARRO_DEEP_LEARNING_BASED_UV_FACIAL_IMAGING_GENERATION_2023.PDF (1.92 MB)
Citações na Scopus
Tipo de produção
conferenceObject
Data de publicação
2023
DOI
Web of Science
Título da Revista
ISSN da Revista
Título do Volume
Editora
IEEE
Autores
MARGALEF, Pablo Toledo
NAVARRO, Pablo
HUNEMEIER, Tabita
GONZALEZ-JOSE, Rolando
DELRIEUX, Claudio
Citação
2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Skin health has become a topic of interest in the recent years. To ensure a better diagnosis and treatment, the analysis of high-quality skin databases is crucial. In this regard, UV imaging is a valuable tool in detecting melanoma and other skin conditions. However, UV images present some challenges both in availability and processing. For this reason, in this work we present UVnet, a method to generate opticalto-UV facial images based on autoencoder architectures. The proposed UVnet is validated across an extension of the Baependi Heart Study and other state of the art method [1]. Our proposal successfully generates pseudo-UV samples with an average RMSE of 0.0040 and a structural similarity index against the actual samples of 0.2984. These results show that UVnet consistently achieves higher sample quality than existing methods and provides new capabilities regarding generation of large areas of the facial epidermis. This can be regarded as an initial effort to provide affordable access to high-quality skin databases.
Palavras-chave
Machine Learning, Unet, UV imaging, facial skin
URI
https://observatorio.fm.usp.br/handle/OPI/57150
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Coleções
Comunicações em Eventos - HC/InCor
Comunicações em Eventos - LIM/13