Clinical Context-aware Radiology Report Generation from Medical Images using Transformers
This work addresses the problem of automating radiology report generation for medical professionals, but it is incremental as it applies an existing transformer method to a specific medical domain.
The authors tackled radiology report generation from chest X-rays using a transformer model, achieving superior results and faster performance compared to LSTM-based methods on the IU-CXR dataset.
Recent developments in the field of Natural Language Processing, especially language models such as the transformer have brought state-of-the-art results in language understanding and language generation. In this work, we investigate the use of the transformer model for radiology report generation from chest X-rays. We also highlight limitations in evaluating radiology report generation using only the standard language generation metrics. We then applied a transformer based radiology report generation architecture, and also compare the performance of a transformer based decoder with the recurrence based decoder. Experiments were performed using the IU-CXR dataset, showing superior results to its LSTM counterpart and being significantly faster. Finally, we identify the need of evaluating radiology report generation system using both language generation metrics and classification metrics, which helps to provide robust measure of generated reports in terms of their coherence and diagnostic value.