  This paper explores methods for extracting information from radiology reports
that generalize across exam modalities to reduce requirements for annotated
data. We demonstrate that multi-pass T5-based text-to-text generative models
exhibit better generalization across exam modalities compared to approaches
that employ BERT-based task-specific classification layers. We then develop
methods that reduce the inference cost of the model, making large-scale corpus
processing more feasible for clinical applications. Specifically, we introduce
a generative technique that decomposes complex tasks into smaller subtask
blocks, which improves a single-pass model when combined with multitask
training. In addition, we leverage target-domain contexts during inference to
enhance domain adaptation, enabling use of smaller models. Analyses offer
insights into the benefits of different cost reduction strategies.
