  Machine learning models are becoming commonplace in the domain of medical
imaging, and with these methods comes an ever-increasing need for more data.
However, to preserve patient anonymity it is frequently impractical or
prohibited to transfer protected health information (PHI) between institutions.
Additionally, due to the nature of some studies, there may not be a large
public dataset available on which to train models. To address this conundrum,
we analyze the efficacy of transferring the model itself in lieu of data
between different sites. By doing so we accomplish two goals: 1) the model
gains access to training on a larger dataset that it could not normally obtain
and 2) the model better generalizes, having trained on data from separate
locations. In this paper, we implement multi-site learning with disparate
datasets from the National Institutes of Health (NIH) and Vanderbilt University
Medical Center (VUMC) without compromising PHI. Three neural networks are
trained to convergence on a computed tomography (CT) brain hematoma
segmentation task: one only with NIH data,one only with VUMC data, and one
multi-site model alternating between NIH and VUMC data. Resultant lesion masks
with the multi-site model attain an average Dice similarity coefficient of 0.64
and the automatically segmented hematoma volumes correlate to those done
manually with a Pearson correlation coefficient of 0.87,corresponding to an 8%
and 5% improvement, respectively, over the single-site model counterparts.
