Szczegóły publikacji
Opis bibliograficzny
CXR-FL: deep learning-based chest X-ray image analysis using federated learning / Filip Ślazyk, Przemysław Jabłecki, Aneta Lisowska, Maciej MALAWSKI, Szymon Płotka // W: Computational Science – ICCS 2022 : 22nd international conference : London, UK, June 21–23, 2022 : proceedings, Pt. 2 / eds. Derek Groen, [et al.]. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13351). — ISBN: 978-3-031-08753-0; e-ISBN: 978-3-031-08754-7. — S. 433–440. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-06-15. — F. Ślazyk, P. Jabłecki, M. Malawski - dod. afiliacja: Sano Centre for Computational Medicine, Krakow
Autorzy (5)
- AGHŚlazyk Filip
- AGHJabłecki Przemysław
- Lisowska Aneta
- AGHMalawski Maciej
- Płotka Szymon
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140665 |
|---|---|
| Data dodania do BaDAP | 2022-08-24 |
| DOI | 10.1007/978-3-031-08754-7_50 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 22nd International Conference on Computational Science |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Federated learning enables building a shared model from multicentre data while storing the training data locally for privacy. In this paper, we present an evaluation (called CXR-FL) of deep learning-based models for chest X-ray image analysis using the federated learning method. We examine the impact of federated learning parameters on the performance of central models. Additionally, we show that classification models perform worse if trained on a region of interest reduced to segmentation of the lung compared to the full image. However, focusing training of the classification model on the lung area may result in improved pathology interpretability during inference. We also find that federated learning helps maintain model generalizability. The pre-trained weights and code are publicly available at (https://github.com/SanoScience/CXR-FL).