Enhanced Dermatology Image Quality Assessment via Cross-Domain Training
Poor image quality severely undermines diagnostic reliability in teledermatology, while existing dermatological image quality assessment (IQA) methods suffer from limited training data, narrow domain coverage, and failure to leverage advances in general-purpose IQA. To address these limitations, we propose a cross-domain joint training framework. We first introduce Legit.Health-DIQA-Artificial—a novel, dermatology-specific synthetic IQA dataset—and jointly train on it alongside large-scale general IQA benchmarks (e.g., LIVE, TID2013). Our approach integrates human subjective quality annotations with deep transfer learning. By bridging domain gaps and enriching supervision signals, the method overcomes the small-sample bottleneck, substantially improving model generalizability and robustness. Extensive evaluation across multiple domains demonstrates state-of-the-art accuracy in dermatological IQA. This work establishes a reliable, scalable foundation for image quality screening in remote dermatological diagnosis and triage.