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Medical imaging is critical to the diagnosis and treatment of numerousmedical problems, including many forms of cancer. Medical imaging reportsdistill the findings and observations of radiologists, creating an unstructuredtextual representation of unstructured medical images. Large-scale use of thistext-encoded information requires converting the unstructured text to astructured, semantic representation. We explore the extraction andnormalization of anatomical information in radiology reports that is associatedwith radiological findings. We investigate this extraction and normalizationtask using a span-based relation extraction model that jointly extractsentities and relations using BERT. This work examines the factors thatinfluence extraction and normalization performance, including the bodypart/organ system, frequency of occurrence, span length, and span diversity. Itdiscusses approaches for improving performance and creating high-qualitysemantic representations of radiological phenomena.

Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model