Chest X-ray Report Generation through Fine-Grained Label Learning
This addresses the need for automated preliminary read reports to expedite clinical workflows in hospitals, though it appears incremental as it builds on existing report generation approaches.
The researchers tackled the problem of generating clinically acceptable automated chest X-ray reports by developing a domain-aware algorithm that learns fine-grained descriptions of radiographic findings and retrieves customized reports from a database, significantly outperforming state-of-the-art methods on established score metrics.
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the quality of reports generated by current automated approaches is not yet clinically acceptable as they cannot ensure the correct detection of a broad spectrum of radiographic findings nor describe them accurately in terms of laterality, anatomical location, severity, etc. In this work, we present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images and uses their pattern of occurrences to retrieve and customize similar reports from a large report database. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings. The resulting report generation algorithm significantly outperforms the state of the art using established score metrics.