Chen, Yung-YaoYung-YaoChenChan, Hung-TseHung-TseChanWang, Hsiao-ChiHsiao-ChiWangWang, Chii-ShyanChii-ShyanWangChen, Hsuan-HsiangHsuan-HsiangChenPO-HUA CHENChen, Yi-JuYi-JuChenHsu, Shao-HsuanShao-HsuanHsuHsia, Chih-HsienChih-HsienHsia2025-07-012025-07-012025-03-26https://scholars.lib.ntu.edu.tw/handle/123456789/730432Accurate 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.enacnedeep learningmedical clinical imagemulti-standardspseudo-labelsemi-supervised learning[SDGs]SDG3journal article10.3390/bioengineering1204034240281702