Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
This work addresses the time-consuming task of writing radiology findings for radiologists, but it is incremental as it builds on existing report generation methods by adding interpretability through a two-step process.
The paper tackles the problem of generating radiology findings from chest X-rays by proposing a two-step approach that detects abnormal regions and uses a generative LLM for text generation, aiming to reduce radiologists' workload by automating part of the report writing process.
Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient. The information included in Impression is also often covered in Findings. While Findings and Impression can be deduced by inspecting the image, Clinical Indications often require additional context. The cognitive task of interpreting medical images remains the most critical and often time-consuming step in the radiology workflow. Instead of generating an end-to-end radiology report, in this paper, we focus on generating the Findings from automated interpretation of medical images, specifically chest X-rays (CXRs). Thus, this work focuses on reducing the workload of radiologists who spend most of their time either writing or narrating the Findings. Unlike past research, which addresses radiology report generation as a single-step image captioning task, we have further taken into consideration the complexity of interpreting CXR images and propose a two-step approach: (a) detecting the regions with abnormalities in the image, and (b) generating relevant text for regions with abnormalities by employing a generative large language model (LLM). This two-step approach introduces a layer of interpretability and aligns the framework with the systematic reasoning that radiologists use when reviewing a CXR.