Generation of Radiology Findings in Chest X-Ray by Leveraging
  Collaborative Knowledge
Manuela Daniela Danu George Marica Sanjeev Kumar Karn Bogdan Georgescu Awais Mansoor Florin Ghesu Lucian Mihai Itu Constantin Suciu Sasa Grbic Oladimeji Farri Dorin Comaniciu
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.