Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

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.This article is protected by copyright. All rights reserved.

Generation of skin tone and pigmented region‐modified images using a pigment discrimination model trained with an optical approach

“…In this study, we utilized the revised training framework and the ResUNet++ model structure. Additionally, in our previous research, 25 we confirmed that applying ground truth for the pigments could further improve performance. However, given the considerable usability benefits of using the optical method in place of the ground truth, we chose not to use ground truth for the pigments in this study.…”