  Vision-language pre-training for chest X-rays has made significant strides,
primarily by utilizing paired radiographs and radiology reports. However,
existing approaches often face challenges in encoding medical knowledge
effectively. While radiology reports provide insights into the current disease
manifestation, medical definitions (as used by contemporary methods) tend to be
overly abstract, creating a gap in knowledge. To address this, we propose
DeViDe, a novel transformer-based method that leverages radiographic
descriptions from the open web. These descriptions outline general visual
characteristics of diseases in radiographs, and when combined with abstract
definitions and radiology reports, provide a holistic snapshot of knowledge.
DeViDe incorporates three key features for knowledge-augmented vision language
alignment: First, a large-language model-based augmentation is employed to
homogenise medical knowledge from diverse sources. Second, this knowledge is
aligned with image information at various levels of granularity. Third, a novel
projection layer is proposed to handle the complexity of aligning each image
with multiple descriptions arising in a multi-label setting. In zero-shot
settings, DeViDe performs comparably to fully supervised models on external
datasets and achieves state-of-the-art results on three large-scale datasets.
Additionally, fine-tuning DeViDe on four downstream tasks and six segmentation
tasks showcases its superior performance across data from diverse
distributions.
