Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels

Artificial intelligence (AI) methods are revolutionizing medical image analysis. However, robust AI models require large multi-site datasets for training. While multiple stakeholders have provided publicly available datasets, the ways in which these data are labeled differ widely. For example, one dataset of chest radiographs might contain labels denoting the presence of metastases in the lung, while another dataset of chest radiograph might focus on the presence of pneumonia. With conventional approaches, these data cannot be used together to train a single AI model. We propose a new framework that we call flexible federated learning (FFL) for collaborative training on such data. Using publicly available data of 695,000 chest radiographs from five institutions - each with differing labels - we demonstrate that large and heterogeneously labeled datasets can be used to train one big AI model with this framework. We find that models trained with FFL are superior to models that are trained on matching annotations only. This may pave the way for training of truly large-scale AI models that make efficient use of all existing data.