Enhanced Dermatology Image Quality Assessment via Cross-Domain Training
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918069611266048 |
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| author | Montilla, Ignacio Hernández Medela, Alfonso Pasquali, Paola Aguilar, Andy Mac Carthy, Taig Fernández, Gerardo Martorell, Antonio Onieva, Enrique |
| author_facet | Montilla, Ignacio Hernández Medela, Alfonso Pasquali, Paola Aguilar, Andy Mac Carthy, Taig Fernández, Gerardo Martorell, Antonio Onieva, Enrique |
| contents | Teledermatology 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_16116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Dermatology Image Quality Assessment via Cross-Domain Training Montilla, Ignacio Hernández Medela, Alfonso Pasquali, Paola Aguilar, Andy Mac Carthy, Taig Fernández, Gerardo Martorell, Antonio Onieva, Enrique Image and Video Processing Computer Vision and Pattern Recognition Multimedia Teledermatology 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. |
| title | Enhanced Dermatology Image Quality Assessment via Cross-Domain Training |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2506.16116 |