  The impression section of a radiology report summarizes the most prominent
observation from the findings section and is the most important section for
radiologists to communicate to physicians. Summarizing findings is
time-consuming and can be prone to error for inexperienced radiologists, and
thus automatic impression generation has attracted substantial attention. With
the encoder-decoder framework, most previous studies explore incorporating
extra knowledge (e.g., static pre-defined clinical ontologies or extra
background information). Yet, they encode such knowledge by a separate encoder
to treat it as an extra input to their models, which is limited in leveraging
their relations with the original findings. To address the limitation, we
propose a unified framework for exploiting both extra knowledge and the
original findings in an integrated way so that the critical information (i.e.,
key words and their relations) can be extracted in an appropriate way to
facilitate impression generation. In detail, for each input findings, it is
encoded by a text encoder, and a graph is constructed through its entities and
dependency tree. Then, a graph encoder (e.g., graph neural networks (GNNs)) is
adopted to model relation information in the constructed graph. Finally, to
emphasize the key words in the findings, contrastive learning is introduced to
map positive samples (constructed by masking non-key words) closer and push
apart negative ones (constructed by masking key words). The experimental
results on OpenI and MIMIC-CXR confirm the effectiveness of our proposed
method.
