| Yazarlar (3) |
Ülkü Tuncer Küçüktaş
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Dr. Öğr. Üyesi Fatih UYSAL
Kafkas Üniversitesi, Türkiye |
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Fırat Hardalaç
Gazi Üniversitesi, Türkiye |
| Özet |
| Federated learning has become a key method in medical research, not only for its effectiveness in handling sensitive data but also for enabling collaboration between different centers while ensuring patient privacy. This study aims to fill a research gap in the use of federated learning in dentistry, a field where the protection of sensitive data is crucial. We compare the performance of federated learning with local and central learning in dental disease detection. Our research focuses on two scenarios: one involving imbalanced data distribution across different centers, and another on continual learning, where the model's ability to learn new classes introduced by a single center is evaluated. Our results indicate that federated learning could be a promising approach, particularly when central learning is not feasible. The findings are especially encouraging in the continual learning scenario, suggesting that federated learning is adept at adapting to new information while maintaining previously acquired knowledge. |
| Anahtar Kelimeler |
| carries | continual learning | deep learning | federated learning | periapical lesion |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayımlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | 11th International Conference on Electrical and Electronics Engineering (ICEEE) |
| Kongre Tarihi | 22-04-2024 / 24-04-2024 |
| Basıldığı Ülke | Türkiye |
| Basıldığı Şehir | Muğla |
| Atıf Sayıları |
