Feature Feedback-Based Pseudo-Label Learning for Multi-Standards in Clinical Acne Grading
Journal
Bioengineering
Journal Volume
12
Journal Issue
4
Start Page
342
ISSN
2306-5354
Date Issued
2025-03-26
Author(s)
Chen, Yung-Yao
Chan, Hung-Tse
Wang, Hsiao-Chi
Wang, Chii-Shyan
Chen, Hsuan-Hsiang
Chen, Yi-Ju
Hsu, Shao-Hsuan
Hsia, Chih-Hsien
Abstract
Accurate acne grading is critical in optimizing therapeutic decisions yet remains challenging due to lesion ambiguity and subjective clinical assessments. This study proposes the Feature Feedback-Based Pseudo-Label Learning (FF-PLL) framework to address these limitations through three innovations: (1) an acne feature feedback (AFF) architecture with iterative pseudo-label refinement to improve the training robustness, enhance the pseudo-label quality, and increase the feature diversity; (2) all-facial skin segmentation (AFSS) to reduce background noise, enabling precise lesion feature extraction; and (3) the AcneAugment (AA) strategy to foster model generalization by introducing diverse acne lesion representations. Experiments on the ACNE04 and ACNE-ECKH benchmark datasets demonstrate the superiority of the proposed framework, achieving accuracy of 87.33% on ACNE04 and 67.50% on ACNE-ECKH. Additionally, the model attains sensitivity of 87.31%, specificity of 90.14%, and a Youden index (YI) of 77.45% on ACNE04. These advancements establish FF-PLL as a clinically viable solution for standardized acne assessment, bridging critical gaps between computational dermatology and practical healthcare needs.
Subjects
acne
deep learning
medical clinical image
multi-standards
pseudo-label
semi-supervised learning
SDGs
Publisher
MDPI AG
Type
journal article