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

TL;DR

This paper introduces a large-scale, disease-aware vision-language model for 3D CT scans that improves retrieval, classification, and intra-scan localization by leveraging extensive data and novel supervision techniques.

Contribution

It presents a unified model trained on 98k report-volume pairs, incorporating disease supervision and intra-scan snippet localization, advancing 3D CT vision-language understanding.

Findings

State-of-the-art text-to-image retrieval performance

Competitive disease classification accuracy

Effective intra-scan snippet localization with reduced error

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.