Feature Feedback-Based Pseudo-Label Learning for Multi-Standards in Clinical Acne Grading

What is it about?

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

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Photo by x ) on Unsplash

Photo by x ) on Unsplash

Why is it important?

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.

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