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Fine-Grained Vision-Language Pretraining with Organ-Conditioned Pattern Tokens for CT Understanding

Research Multimodal & Generative

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TL;DR — OCP-CT is a vision-language pretraining framework that aligns 3D CT volumes with radiology reports at the fine-grained level of "organ-conditioned radiological patterns," improving zero-shot abnormality diagnosis over prior methods.

  • Motivation: global scan-report contrast obscures per-organ evidence, while direct organ-level alignment is too coarse since one anatomy can present multiple distinct radiological appearances.
  • Method: keeps a stable global CT-report contrastive branch plus an "organ pattern interface" — sparse Mixture-of-Experts routes image/text tokens by latent radiological pattern, learnable slots query them into continuous pattern tokens, and paired token contrast aligns image-text pattern tokens using soft targets from report-derived clinical similarity.
  • Results: zero-shot abnormality diagnosis average AUROC of 84.5% on CT-RATE and 69.9% on RAD-ChestCT, absolute gains of +6.7 and +0.8 points over the strongest prior reported results.
  • Note: cross-modal (3D CT imaging + text) medical VLM, evaluated on the public CT-RATE and RAD-ChestCT benchmarks.

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Fine-Grained Vision-Language Pretraining with Organ-Conditioned Pattern Tokens for CT Understanding

arXiv cs.CV Guoliang You, Xiaomeng Chu 2026-07-15 arXiv:2607.13892
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-05 14:30:29.168354 UTC

TL;DR — OCP-CT is a vision-language pretraining framework that aligns 3D CT volumes with radiology reports at the fine-grained level of "organ-conditioned radiological patterns," improving zero-shot abnormality diagnosis over prior methods.

  • Motivation: global scan-report contrast obscures per-organ evidence, while direct organ-level alignment is too coarse since one anatomy can present multiple distinct radiological appearances.
  • Method: keeps a stable global CT-report contrastive branch plus an "organ pattern interface" — sparse Mixture-of-Experts routes image/text tokens by latent radiological pattern, learnable slots query them into continuous pattern tokens, and paired token contrast aligns image-text pattern tokens using soft targets from report-derived clinical similarity.
  • Results: zero-shot abnormality diagnosis average AUROC of 84.5% on CT-RATE and 69.9% on RAD-ChestCT, absolute gains of +6.7 and +0.8 points over the strongest prior reported results.
  • Note: cross-modal (3D CT imaging + text) medical VLM, evaluated on the public CT-RATE and RAD-ChestCT benchmarks.
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