Back to Search
Start Over
A Federated Learning Approach to Tumor Detection in Colon Histology Images.
- Source :
-
Journal of medical systems [J Med Syst] 2023 Sep 16; Vol. 47 (1), pp. 99. Date of Electronic Publication: 2023 Sep 16. - Publication Year :
- 2023
-
Abstract
- Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images.<br /> (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Details
- Language :
- English
- ISSN :
- 1573-689X
- Volume :
- 47
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Journal of medical systems
- Publication Type :
- Academic Journal
- Accession number :
- 37715855
- Full Text :
- https://doi.org/10.1007/s10916-023-01994-5