Hernández Montilla, IgnacioMedela, AlfonsoPasquali, PaolaAguilar, AndyMac Carthy, TaigFernández, GerardoMartorell, AntonioOnieva Caracuel, Enrique2026-04-202026-04-202025-12-22Hernández Montilla, I., Medela, A., Pasquali, P., Aguilar, A., Mac Carthy, T., Fernández, G., Martorell, A., & Onieva, E. (2025). Enhanced dermatology Image Quality Assessment via cross-domain training. ICBRA 2025 - Proceedings of the 12th International Conference on Bioinformatics Research and Applications, 1-9. https://doi.org/10.1145/3774976.3774977979840071580810.1145/3774976.3774977https://hdl.handle.net/20.500.14454/5692Ponencia presentada en la ICBRA 2025: The 12th International Conference on Bioinformatics Research and Applications, celebrada en Praga, entre el 19 y el 21 de septiembre de 2025Teledermatology has become a widely accepted communication method in daily clinical practice, enabling remote care while showing strong agreement with in-person visits. Poor image quality remains an unsolved problem in teledermatology and is a major concern to practitioners, as bad-quality images reduce the usefulness of the remote consultation process. However, research on Image Quality Assessment (IQA) in dermatology is sparse, and does not leverage the latest advances in non-dermatology IQA, such as using larger image databases with ratings from large groups of human observers. In this work, we propose cross-domain training of IQA models, combining dermatology and non-dermatology IQA datasets. For this purpose, we created a novel dermatology IQA database, Legit.Health-DIQA-Artificial, using dermatology images from several sources and having them annotated by a group of human observers. We demonstrate that cross-domain training yields optimal performance across domains and overcomes one of the biggest limitations in dermatology IQA, which is the small scale of data, and leads to models trained on a larger pool of image distortions, resulting in a better management of image quality in the teledermatology process.eng© 2025 Copyright held by the owner/author(s)Computer visionDermatologyImage Quality AssessmentTeledermatologyconference paper2026-04-20