Learning to Read Where to Look: Disease-Aware Vision-Language Pretraining for 3D CT

TL;DR

A 3D CT vision-language model is trained on 98k report-volume pairs collected at a single hospital, combined with public datasets, using SigLIP-style contrastive pretraining together with prompt-based disease supervision in the shared vision-text embedding space, yielding a single unified model for retrieval, classification, and intra-scan grounding.

Abstract

Recent 3D CT vision-language models align volumes with reports via contrastive pretraining, but typically rely on limited public data and provide only coarse global supervision. We train a 3D CT vision-language model on 98k report-volume pairs (50k patients) collected at a single hospital, combined with public datasets, using SigLIP-style contrastive pretraining together with prompt-based disease supervision in the shared vision-text embedding space. On CT-RATE, our model achieves state-of-the-art text-to-image retrieval (R@10 31.5 vs. 22.2) and competitive disease classification (AUC 83.8 vs. 83.8), with consistent results on Rad-ChestCT (AUC 77.0 vs. 77.3). We further observe that radiologists routinely reference specific images within their reports (e.g., ``series X, image Y''), linking textual descriptions to precise axial locations. We automatically mine 262k such snippet-slice pairs and introduce the task of intra-scan snippet localization -- predicting the axial depth referred to by a text snippet -- reducing mean absolute error to 36.3 mm at 12 mm feature resolution, compared with 67.0 mm for the best baseline. Adding this localization objective leaves retrieval and classification broadly unchanged within confidence bounds, yielding a single unified model for retrieval, classification, and intra-scan grounding.

Figures (1)

• Figure 1: Overview of the RadFinder architecture and training pipeline. Left (Vision): A 3D CT scan is processed by a frozen parallel window encoder, followed by a trainable encoder to extract both a global volume embedding and local slice-level embeddings. Right (Language): A trainable text encoder processes full radiology reports, localized text snippets (e.g., the sentence that references the fourth slice out of the 32 slices in this example scan), and LLM-extracted positive/negative disease prompts. Center (Training): The model is optimized via three contrastive objectives in a shared embedding space: a global Contrastive Loss aligning the full scan and report, an intra-scan Localization Loss aligning text snippets with their specific slices, and a Disease Prompt Loss aligning the global scan with corresponding disease prompt descriptions