  A major limitation in applying deep learning to artificial intelligence (AI)
systems is the scarcity of high-quality curated datasets. We investigate strong
augmentation based self-supervised learning (SSL) techniques to address this
problem. Using breast cancer detection as an example, we first identify a
mammogram-specific transformation paradigm and then systematically compare four
recent SSL methods representing a diversity of approaches. We develop a method
to convert a pretrained model from making predictions on uniformly tiled
patches to whole images, and an attention-based pooling method that improves
the classification performance. We found that the best SSL model substantially
outperformed the baseline supervised model. The best SSL model also improved
the data efficiency of sample labeling by nearly 4-fold and was highly
transferrable from one dataset to another. SSL represents a major breakthrough
in computer vision and may help the AI for medical imaging field to shift away
from supervised learning and dependency on scarce labels.
