  With promising results of machine learning based models in computer vision,
applications on medical imaging data have been increasing exponentially.
However, generalizations to complex real-world clinical data is a persistent
problem. Deep learning models perform well when trained on standardized
datasets from artificial settings, such as clinical trials. However, real-world
data is different and translations are yielding varying results. The complexity
of real-world applications in healthcare could emanate from a mixture of
different data distributions across multiple device domains alongside the
inevitable noise sourced from varying image resolutions, human errors, and the
lack of manual gradings. In addition, healthcare applications not only suffer
from the scarcity of labeled data, but also face limited access to unlabeled
data due to HIPAA regulations, patient privacy, ambiguity in data ownership,
and challenges in collecting data from different sources. These limitations
pose additional challenges to applying deep learning algorithms in healthcare
and clinical translations. In this paper, we utilize self-supervised
representation learning methods, formulated effectively in transfer learning
settings, to address limited data availability. Our experiments verify the
importance of diverse real-world data for generalization to clinical settings.
We show that by employing a self-supervised approach with transfer learning on
a multi-domain real-world dataset, we can achieve 16% relative improvement on a
standardized dataset over supervised baselines.
