  Medical imaging is critical to the diagnosis and treatment of numerous
medical problems, including many forms of cancer. Medical imaging reports
distill the findings and observations of radiologists, creating an unstructured
textual representation of unstructured medical images. Large-scale use of this
text-encoded information requires converting the unstructured text to a
structured, semantic representation. We explore the extraction and
normalization of anatomical information in radiology reports that is associated
with radiological findings. We investigate this extraction and normalization
task using a span-based relation extraction model that jointly extracts
entities and relations using BERT. This work examines the factors that
influence extraction and normalization performance, including the body
part/organ system, frequency of occurrence, span length, and span diversity. It
discusses approaches for improving performance and creating high-quality
semantic representations of radiological phenomena.
