Acne severity grading is an important dermatological task that supports clinical diagnosis, treatment planning, and disease monitoring. Although self-supervised learning (SSL) has gained interest as a means to reduce reliance on large annotated datasets, its effectiveness for fine-grained and ordinal dermatological tasks remains unclear. This research systematically evaluates contrastive SSL for acne severity grading by comparing SimCLR-based pretraining with a diverse set of supervised deep learning models, including Convolutional Neural Networks and Vision Transformers, under controlled experimental conditions. The evaluation considers full-data training, label-scarce scenarios, and temperature tuning of the contrastive loss. The results consistently demonstrate the superiority of supervised transfer learning, which achieves Quadratic Weighted Kappa (QWK) scores ranging from 0.7616 to 0.8533. In contrast, SimCLR-based models exhibit substantially lower performance, with QWK values between 0.2343 and 0.4548 after fine-tuning. Although temperature adjustment achieved modest performance gains, it does not close this gap, with the best configuration attaining a QWK of 0.4548 using a ResNet18 backbone. Qualitative analysis using Grad-CAM further reveals that SimCLR-based contrastive SSL tends to exhibit diffuse attention patterns and limited localization of clinically relevant acne regions. Overall, these findings indicate that generic contrastive SSL objectives are poorly aligned with the subtle and localized visual cues required for acne severity grading. The results highlight the need for domain-aware representation learning in fine-grained dermatological image analysis.
Srijiranon et al. (Thu,) studied this question.