SS-ALDL: Consistency-based semi-supervised label distribution learning for acne severity classification

Acne vulgaris is a common skin disease among adolescents. Accurate classification of acne severity is critical to patient treatment. Most existing acne severity classification models ignore the number of acne lesions and the similarity between samples. Moreover, training a supervised model requires collecting a large amount of labeled data, which is labor-intensive and time-consuming. To solve the above problems, this study presents a consistency-based semi-supervised label distribution learning (SS-ALDL) framework, which is the first acne- specific semi-supervised framework for acne severity classification. It generates three distributions based on acne grading criteria, including acne severity, lesion counts, and grading transformed by counts. These three distributions are integrated by multi-task learning loss and optimized in supervised training for joint acne image grading and counting. Furthermore, a feature similarity consistency learning method is proposed for semi-supervised training. By maintaining the batch-level feature similarity matrix between different samples under different perturbations, the proposed method can effectively explore extra semantic information from the unlabeled data. The performance of the proposed model is evaluated on the ACNE04 dataset, the RetinaMNIST dataset, and a private dataset. It achieves the best classification accuracy and the lowest mean absolute error. These experimental results show that the proposed method outperforms other state-of-the-art semi-supervised methods and can significantly reduce the manual assessment workload.