Fine-Grained Vision-Language Pretraining with Organ-Conditioned Pattern Tokens for CT Understanding
This work addresses the need for finer-grained alignment in CT vision-language pretraining, benefiting medical image analysis by improving zero-shot abnormality diagnosis.
OCP-CT introduces organ-conditioned pattern-token alignment for CT vision-language pretraining, achieving average AUROCs of 84.5% and 69.9% on CT-RATE and RAD-ChestCT benchmarks, with absolute gains of 6.7 and 0.8 percentage points over prior methods.
Computed tomography (CT) vision-language pretraining from paired volumes and radiology reports is a scalable yet challenging task. Existing methods commonly adopt global scan-report contrast, which is scalable but obscures heterogeneous organ evidence. Meanwhile, direct organ-level alignment remains coarse, since the same anatomy can exhibit multiple distinct radiological appearances. Therefore, pretraining requires a finer alignment unit: the organ-conditioned radiological pattern. In this work, we propose OCP-CT, an organ-conditioned pattern-token alignment framework for CT vision-language pretraining. Specifically, OCP-CT preserves a stable global CT-report contrastive branch and introduces an organ pattern interface: sparse Mixture-of-Experts (MoE) routes image and text tokens according to latent radiological patterns, learnable slots query the routed tokens into continuous pattern tokens, and paired token contrast aligns image-text pattern tokens with structured soft targets built from report-derived clinical similarity. On the publicly available CT-RATE and RAD-ChestCT benchmarks, OCP-CT achieves average AUROCs of 84.5% and 69.9% for zero-shot abnormality diagnosis, respectively. Compared with the strongest prior reported results, these results yield absolute AUROC gains of 6.7 and 0.8 percentage points.