  Building accurate and robust artificial intelligence systems for medical
image assessment requires not only the research and design of advanced deep
learning models but also the creation of large and curated sets of annotated
training examples. Constructing such datasets, however, is often very costly --
due to the complex nature of annotation tasks and the high level of expertise
required for the interpretation of medical images (e.g., expert radiologists).
To counter this limitation, we propose a method for self-supervised learning of
rich image features based on contrastive learning and online feature
clustering. For this purpose we leverage large training datasets of over
100,000,000 medical images of various modalities, including radiography,
computed tomography (CT), magnetic resonance (MR) imaging and ultrasonography.
We propose to use these features to guide model training in supervised and
hybrid self-supervised/supervised regime on various downstream tasks. We
highlight a number of advantages of this strategy on challenging image
assessment problems in radiography, CT and MR: 1) Significant increase in
accuracy compared to the state-of-the-art (e.g., AUC boost of 3-7% for
detection of abnormalities from chest radiography scans and hemorrhage
detection on brain CT); 2) Acceleration of model convergence during training by
up to 85% compared to using no pretraining (e.g., 83% when training a model for
detection of brain metastases in MR scans); 3) Increase in robustness to
various image augmentations, such as intensity variations, rotations or scaling
reflective of data variation seen in the field.
