CLARIFID: Improving Radiology Report Generation by Reinforcing Clinically Accurate Impressions and Enforcing Detailed Findings

TL;DR

CLARIFID is a novel framework for radiology report generation that emphasizes clinical accuracy and detailed findings by mirroring expert workflows, using multi-view imaging, and optimizing for factual correctness.

Contribution

The paper introduces CLARIFID, a new approach that directly optimizes diagnostic correctness and integrates multi-view imaging with a workflow-inspired training process.

Findings

Outperforms existing baselines on clinical efficacy scores

Achieves higher CheXbert F1 scores for report accuracy

Ensures coherent clinical reasoning in generated reports

Abstract

Automatic generation of radiology reports has the potential to alleviate radiologists' significant workload, yet current methods struggle to deliver clinically reliable conclusions. In particular, most prior approaches focus on producing fluent text without effectively ensuring the factual correctness of the reports and often rely on single-view images, limiting diagnostic comprehensiveness. We propose CLARIFID, a novel framework that directly optimizes diagnostic correctness by mirroring the two-step workflow of experts. Specifically, CLARIFID (1) learns the logical flow from Findings to Impression through section-aware pretraining, (2) is fine-tuned with Proximal Policy Optimization in which the CheXbert F1 score of the Impression section serves as the reward, (3) employs controlled decoding that completes "Findings" before synthesizing the "Impression", and (4) fuses multiple chest X-ray views via a vision-transformer-based multi-view encoder. During inference, we apply a next-token forcing strategy followed by report-level re-ranking, ensuring that the model first produces a comprehensive "Findings" section before synthesizing the "Impression" and thereby preserving coherent clinical reasoning. Experimental results on the MIMIC-CXR dataset demonstrate that our method achieves superior clinical efficacy and outperforms existing baselines on clinical efficacy scores.