Training of AI Models Beyond the Local Dataset Using Federated Learning with 695,000 Non-Identically-Labeled Chest Radiographs

Zielsetzung Artificial intelligence (AI) models require large annotated datasets for training. While federated learning (FL) enables multiple institutions to securely cooperate in training AI models, it requires all images to be annotated identically and based on the same classifications and conditions. In the real world, this can usually only be achieved if partners agree on a standardized annotation beforehand. Our aim was to develop and validate an extension to FL for collaborative training of chest radiographs that had not been labeled identically.