Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909785951043584 |
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| author | Ünal, Ali Burak Baykara, Cem Ata Krawitz, Peter Akgün, Mete |
| author_facet | Ünal, Ali Burak Baykara, Cem Ata Krawitz, Peter Akgün, Mete |
| contents | Machine learning has shown promise in facial dysmorphology, where characteristic facial features provide diagnostic clues for rare genetic disorders. GestaltMatcher, a leading framework in this field, has demonstrated clinical utility across multiple studies, but its reliance on centralized datasets limits further development, as patient data are siloed across institutions and subject to strict privacy regulations. We introduce a federated GestaltMatcher service based on a cross-silo horizontal federated learning framework, which allows hospitals to collaboratively train a global ensemble feature extractor without sharing patient images. Patient data are mapped into a shared latent space, and a privacy-preserving kernel matrix computation framework enables syndrome inference and discovery while safeguarding confidentiality. New participants can directly benefit from and contribute to the system by adopting the global feature extractor and kernel configuration from previous training rounds. Experiments show that the federated service retains over 90% of centralized performance and remains robust to both varying silo numbers and heterogeneous data distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10635 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning Ünal, Ali Burak Baykara, Cem Ata Krawitz, Peter Akgün, Mete Machine Learning Cryptography and Security Computer Vision and Pattern Recognition Machine learning has shown promise in facial dysmorphology, where characteristic facial features provide diagnostic clues for rare genetic disorders. GestaltMatcher, a leading framework in this field, has demonstrated clinical utility across multiple studies, but its reliance on centralized datasets limits further development, as patient data are siloed across institutions and subject to strict privacy regulations. We introduce a federated GestaltMatcher service based on a cross-silo horizontal federated learning framework, which allows hospitals to collaboratively train a global ensemble feature extractor without sharing patient images. Patient data are mapped into a shared latent space, and a privacy-preserving kernel matrix computation framework enables syndrome inference and discovery while safeguarding confidentiality. New participants can directly benefit from and contribute to the system by adopting the global feature extractor and kernel configuration from previous training rounds. Experiments show that the federated service retains over 90% of centralized performance and remains robust to both varying silo numbers and heterogeneous data distributions. |
| title | Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning |
| topic | Machine Learning Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.10635 |