Does Grounding Improve Radiology Report Generation? An Empirical Study on PadChest-GR

Mohamed Aas-Alas, Alberto Albiol, Roberto Paredes

Keywords: Radiology Report Generation, Spatial Grounding, Region-to-Text Generation, Chest X-ray Interpretation

TL;DR: This study shows that injecting spatial grounding into a RRG model substantially boosts linguistic and clinical performance on PadChest-GR.

Abstract: Radiology Report Generation (RRG) aims to automatically produce clinically accurate descriptions of medical images, yet current models often struggle with incomplete findings, generic phrasing, and hallucinations due to the absence of explicit grounding signals. To address these limitations, we propose a grounding-based RRG framework that integrates spatially localized visual evidence into the generation process. Our approach combines a vision encoder ViT with a language decoder LLM GPT-2 through a lightweight transformer-based bridging module inspired by Bridge-Enhanced Vision Encoder–Decoder (VED) architectures. Grounding is introduced using bounding boxes of anatomical regions and pathologies, enabling the model to attend to both global and localized features. We further define a region-to-text task, where the model generates findings directly from specific regions of interest. Experiments on the PadChest-GR dataset demonstrate that grounding substantially improves linguistic quality and clinical accuracy, with the full image plus grounding mask configuration achieving the strongest gains across BLEU, ROUGE-L, CIDEr, BERTScore, CheXbert F1, and RadGraph F1. Incremental masking analyses show that even partial grounding yields consistent benefits.

Primary Subject Area: Generative Models

Secondary Subject Area: Application: Radiology

Registration Requirement: Yes

Reproducibility: https://github.com/LightVED-prhlt/RegionFormer

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

Submission Number: 109