تقرير
Siloed Federated Learning for Multi-Centric Histopathology Datasets
| العنوان: | Siloed Federated Learning for Multi-Centric Histopathology Datasets |
|---|---|
| المؤلفون: | Andreux, Mathieu, Terrail, Jean Ogier du, Beguier, Constance, Tramel, Eric W. |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing |
| الوصف: | While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common. Building on previous domain adaptation works, this paper proposes a novel federated learning approach for deep learning architectures via the introduction of local-statistic batch normalization (BN) layers, resulting in collaboratively-trained, yet center-specific models. This strategy improves robustness to data heterogeneity while also reducing the potential for information leaks by not sharing the center-specific layer activation statistics. We benchmark the proposed method on the classification of tumorous histopathology image patches extracted from the Camelyon16 and Camelyon17 datasets. We show that our approach compares favorably to previous state-of-the-art methods, especially for transfer learning across datasets. Comment: Accepted to MICCAI 2020 DCL workshop |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2008.07424Test |
| رقم الانضمام: | edsarx.2008.07424 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |