  Modern studies in radiograph representation learning rely on either
self-supervision to encode invariant semantics or associated radiology reports
to incorporate medical expertise, while the complementarity between them is
barely noticed. To explore this, we formulate the self- and report-completion
as two complementary objectives and present a unified framework based on masked
record modeling (MRM). In practice, MRM reconstructs masked image patches and
masked report tokens following a multi-task scheme to learn knowledge-enhanced
semantic representations. With MRM pre-training, we obtain pre-trained models
that can be well transferred to various radiography tasks. Specifically, we
find that MRM offers superior performance in label-efficient fine-tuning. For
instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data,
outperforming previous R$^2$L methods with 100% labels. On NIH ChestX-ray, MRM
outperforms the best performing counterpart by about 3% under small labeling
ratios. Besides, MRM surpasses self- and report-supervised pre-training in
identifying the pneumonia type and the pneumothorax area, sometimes by large
margins.
