Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
This work addresses data scarcity and privacy concerns in medical image analysis, enabling collaborative model training across institutions, though it is incremental as it applies an existing federated learning approach to a specific domain.
The study tackled the challenge of training deep learning models for medical image segmentation without centralized data due to privacy and legal issues by using federated learning between two institutions, resulting in models with higher generalizability compared to local training alone.
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.