Deep learning-based pigment analysis model trained with optical approach and ground truth assistance.
J Biophotonics
; 16(12): e202300231, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-37602740
ABSTRACT
This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth-like outputs, the input image resolution is restricted by computational resources. The optical approach-based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach-based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment-modified images for applications like simulating treatment effects.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Biophotonics
Assunto da revista:
BIOFISICA
Ano de publicação:
2023
Tipo de documento:
Article