DiffVP:Differential Visual Semantic Prompting for LLM-Based CT Report Generation

While large language models (LLMs) have advanced chestCT report generation, existing methods typically encode 3D volumesholistically, failing to distinguish informative cues from redundant anatom-ical background. Inspired by radiological cognitive subtraction, we pro-pose Differential Visual Prompting (DiffVP), which conditions reportgeneration on explicit, high-level semantic scan-to-reference differencesrather than solely on absolute visual features. DiffVP employs a hier-archical difference extractor to capture complementary global and localsemantic discrepancies into a shared latent space, along with a difference-to-prompt generator that transforms these signals into learnable visualprefix tokens for LLM conditioning. These difference prompts serve asstructured conditioning signals that implicitly suppress invariant anatomywhile amplifying diagnostically relevant visual evidence, thereby facil-itating accurate report generation without explicit lesion localization.On two large-scale benchmarks, DiffVP consistently outperforms priormethods, improving the average BLEU-1-4 by +10.98 and +4.36, respec-tively, and further boosts clinical efficacy on RadGenome-ChestCT (F1score 0.421). Code will be available at https://github.com/ArielTYH/DiffVP/.