  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.
