  Due to the rapid advancements in recent years, medical image analysis is
largely dominated by deep learning (DL). However, building powerful and robust
DL models requires training with large multi-party datasets. While multiple
stakeholders have provided publicly available datasets, the ways in which these
data are labeled vary widely. For Instance, an institution might provide a
dataset of chest radiographs containing labels denoting the presence of
pneumonia, while another institution might have a focus on determining the
presence of metastases in the lung. Training a single AI model utilizing all
these data is not feasible with conventional federated learning (FL). This
prompts us to propose an extension to the widespread FL process, namely
flexible federated learning (FFL) for collaborative training on such data.
Using 695,000 chest radiographs from five institutions from across the globe -
each with differing labels - we demonstrate that having heterogeneously labeled
datasets, FFL-based training leads to significant performance increase compared
to conventional FL training, where only the uniformly annotated images are
utilized. We believe that our proposed algorithm could accelerate the process
of bringing collaborative training methods from research and simulation phase
to the real-world applications in healthcare.
