论文标题
使用多机构协作深度学习的自动化胰腺细分
Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
论文作者
论文摘要
基于深度学习的方法的性能强烈依赖于用于培训的数据集数量。已经做出了许多努力，以增加医学图像分析字段中的数据。但是，与摄影图像不同，由于许多技术，法律和隐私问题，很难生成集中式数据库来收集医疗图像。在这项工作中，我们研究了现实环境中两个机构之间联合学习的使用，以协作训练模型，而无需在国界共享原始数据。我们定量比较单独使用联合学习和本地培训获得的分割模型。我们的实验结果表明，联合学习模型的概括性比独立培训更高。
The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.
