SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation
Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image. This allows models to achieve competitive scores by exploiting learned priors or spurious correlations, a failure mode we refer to as vision shortcut. We introduce SHOVIR, a benchmark for evaluating vision shortcut behavior in RRG. SHOVIR extends two spatially annotated chest X-ray datasets, MIMIC-CXR and PadChest-GR, with per-box CheXpert labels, and defines im...
| Authors | Filippo Ruffini, Marco Salmé, Rosa Sicilia et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 June 2026 |
| Last updated at source | 22 July 2026 |
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