Chest X-ray Report Generation through Fine-Grained Label Learning

TL;DR

This paper introduces a domain-aware deep learning approach for generating detailed chest X-ray reports by learning fine-grained findings and retrieving similar reports, significantly improving report quality over existing methods.

Contribution

It proposes a novel report generation algorithm that learns fine-grained radiographic descriptions and uses report retrieval to enhance accuracy, addressing limitations of prior approaches.

Findings

Outperforms state-of-the-art methods on established metrics

Effectively recognizes both coarse and fine-grained findings

Improves clinical report quality and detail

Abstract

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.