Expert Insight-Enhanced Follow-up Chest X-Ray Summary Generation

TL;DR

This paper introduces a transformer-based framework for generating follow-up chest X-ray summaries, emphasizing expert insight mechanisms to improve accuracy in reporting disease progression and device changes.

Contribution

It proposes novel expert guidance and masked entity loss mechanisms to enhance follow-up radiology report generation, addressing a gap in existing automatic report systems.

Findings

Model achieves state-of-the-art performance

Expert mechanisms improve summary fidelity

Effective in capturing disease progression details

Abstract

A chest X-ray radiology report describes abnormal findings not only from X-ray obtained at current examination, but also findings on disease progression or change in device placement with reference to the X-ray from previous examination. Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings. To the best of the authors' knowledge, there is only one work dedicated to generating summary of the latter findings, i.e., follow-up summary. In this study, we therefore propose a transformer-based framework to tackle this task. Motivated by our observations on the significance of medical lexicon on the fidelity of summary generation, we introduce two mechanisms to bestow expert insight to our model, namely expert soft guidance and masked entity modeling loss. The former mechanism employs a pretrained expert disease classifier to guide the presence level of specific abnormalities, while the latter directs the model's attention toward medical lexicon. Extensive experiments were conducted to demonstrate that the performance of our model is competitive with or exceeds the state-of-the-art.