  Medical vision-language pre-training has emerged as a promising approach for
learning domain-general representations of medical image and text. Current
algorithms that exploit the global and local alignment between medical image
and text could however be marred by the redundant information in medical data.
To address this issue, we propose a grounded knowledge-enhanced medical
vision-language pre-training (GK-MVLP) framework for chest X-ray. In this
framework, medical knowledge is grounded to the appropriate anatomical regions
by using a transformer-based grounded knowledge-enhanced module for
fine-grained alignment between anatomical region-level visual features and the
textural features of medical knowledge. The performance of GK-MVLP is
competitive with or exceeds the state of the art on downstream chest X-ray
disease classification, disease localization, report generation, and medical
visual question-answering tasks. Our results show the advantage of
incorporating grounding mechanism to remove biases and improve the alignment
between chest X-ray image and radiology report.
