Optimizing Skin Disease Diagnosis: Harnessing Online Community Data With Contrastive Learning and Clustering Techniques

In this study, researchers developed a deep-learning model to diagnose 22 common skin diseases using unannotated dermatology images from online health forums in China. They employed contrastive learning to extract general features from the images and fine-tuned the model with coarsely annotated images from the forums. To enhance the quality of annotations, they applied clustering techniques with a set of standardized validation images. The model was tested on images from 33 dermatologists across 15 tertiary hospitals, achieving a top-1 accuracy of 45.05%, which is a 3% improvement over the baseline model. The accuracy improved to 49.64% when 50 validation images per category were used. The model also showed promising results in detecting monkeypox with a 61.76% top-1 accuracy after being trained with an additional 50 images. The study demonstrates the utility of online forum images for dermatology and the model's potential for early diagnosis and outbreak response.