Surgical Aggregation: A Collaborative Learning Framework for Harmonizing Distributed Medical Imaging Datasets with Diverse Tasks

Large-scale chest x-ray datasets have been curated for the detection of abnormalities using deep learning, with the potential to provide substantial benefits across many clinical applications. However, each dataset focuses only on detecting a subset of findings that can be simultaneously present in a patient, thereby limiting its clinical utility. Therefore, data harmonization is crucial to leverage these datasets in aggregate to train clinically-useful, robust models with a complete representation of all abnormalities that may occur within the thorax. To that end, we propose surgical aggregation, a collaborative learning framework for harmonizing and aggregating knowledge from distributed heterogeneous datasets with partial disease annotations. We evaluate surgical aggregation across synthetic iid datasets and real-world large-scale non-iid datasets with partial annotations. Our results indicate that surgical aggregation significantly outperforms current strategies, has better generalizability, and has the potential to revolutionize the development clinically-useful models as AI-assisted disease characterization becomes a mainstay in radiology.