Enhanced Dermatology Image Quality Assessment via Cross-Domain Training
A cross-domain image-quality study combining dermatology and general-purpose datasets to improve robustness with limited clinical data.
Enhanced Dermatology Image Quality Assessment via Cross-Domain Training
Authors
Ignacio Hernández Montilla, Alfonso Medela, Paola Pasquali, Andy Aguilar, Taig Mac Carthy, Gerardo Fernández, Antonio Martorell, Enrique Onieva.
Abstract
Reliable image-quality assessment is important when dermatology images are captured across different devices and settings. This work combines dermatology and non-dermatology image-quality datasets and introduces Legit.Health-DIQA-Artificial, assembled from several dermatology sources and annotated by human observers.
The experiments found that cross-domain training produced the best performance across domains and helped address the small scale and varied distortions of available dermatology data.
Publication
Published as a peer-reviewed conference paper in the proceedings of ICBRA 2025, pages 1-9.
Available at https://doi.org/10.1145/3774976.3774977.
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