# X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning

arXiv: https://arxiv.org/abs/2503.02162 · HTML: https://mlsanity.com/paper/2503.02162/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing](https://mlsanity.com/paper/2204.09817/) (2022)
- [ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding](https://mlsanity.com/paper/2212.05171/) (2022)
- [ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding](https://mlsanity.com/paper/2305.08275/) (2023)
- [CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training](https://mlsanity.com/paper/2310.13292/) (2023)
- [LLMs Accelerate Annotation for Medical Information Extraction](https://mlsanity.com/paper/2312.02296/) (2023)
- [Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes](https://mlsanity.com/paper/2002.04752/) (2020)
- [GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes](https://mlsanity.com/paper/2305.16037/) (2023)
- [3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography](https://mlsanity.com/paper/2502.02779/) (2025)
- [Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba](https://mlsanity.com/paper/2405.18533/) (2024)
- [MEDBind: Unifying Language and Multimodal Medical Data Embeddings](https://mlsanity.com/paper/2403.12894/) (2024)

## Built on by (most cited first, 2 of 2)

- [ProbMed: A Probabilistic Framework for Medical Multimodal Binding](https://mlsanity.com/paper/2509.25711/) (2025)
- [GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training](https://mlsanity.com/paper/2603.06467/) (2026)

## Abstract (arXiv)

Computed tomography (CT) is a key imaging modality for diagnosis, yet its clinical utility is marred by high radiation exposure and long turnaround times, restricting its use for larger-scale screening. Although chest radiography (CXR) is more accessible and safer, existing CXR foundation models focus primarily on detecting diseases that are readily visible on the CXR. Recently, works have explored training disease classification models on simulated CXRs, but they remain limited to recognizing a single disease type from CT. CT foundation models have also emerged with significantly improved detection of pathologies in CT. However, the generalized application of CT-derived labels on CXR has remained illusive. In this study, we propose X2CT-CLIP, a tri-modal knowledge transfer learning framework that bridges the modality gap between CT and CXR while reducing the computational burden of model training. Our approach is the first work to enable multi-abnormality classification in CT, using CXR, by transferring knowledge from 3D CT volumes and associated radiology reports to a CXR encoder via a carefully designed tri-modal alignment mechanism in latent space. Extensive evaluations on three multi-label CT datasets demonstrate that our method outperforms state-of-the-art baselines in cross-modal retrieval, few-shot adaptation, and external validation. These results highlight the potential of CXR, enriched with knowledge derived from CT, as a viable efficient alternative for disease detection in resource-limited settings.