# Vision Foundation Models for Computed Tomography

arXiv: https://arxiv.org/abs/2501.09001 · HTML: https://mlsanity.com/paper/2501.09001/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2025.

## Built on by (most cited first, 30 of 47)

- [Comprehensive language-image pre-training for 3D medical image understanding](https://mlsanity.com/paper/2510.15042/) (2025)
- [MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images](https://mlsanity.com/paper/2602.06965/) (2026)
- [3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography](https://mlsanity.com/paper/2502.02779/) (2025)
- [CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation](https://mlsanity.com/paper/2508.05375/) (2025)
- [DINOv3 with Test-Time Training for Medical Image Registration](https://mlsanity.com/paper/2508.14809/) (2025)
- [Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging](https://mlsanity.com/paper/2506.18434/) (2025)
- [Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining](https://mlsanity.com/paper/2605.21906/) (2026)
- [Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans](https://mlsanity.com/paper/2510.10779/) (2025)
- [Unveiling and Bridging the Functional Perception Gap in MLLMs: Atomic Visual Alignment and Hierarchical Evaluation via PET-Bench](https://mlsanity.com/paper/2601.02737/) (2026)
- [Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language](https://mlsanity.com/paper/2603.02790/) (2026)
- [Learning Robust Visual Features in Computed Tomography Enables Efficient Transfer Learning for Clinical Tasks](https://mlsanity.com/paper/2604.04133/) (2026)
- [Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging](https://mlsanity.com/paper/2503.19005/) (2025)
- [M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision](https://mlsanity.com/paper/2509.01360/) (2025)
- [TAP-CT: 3D Task-Agnostic Pretraining of Computed Tomography Foundation Models](https://mlsanity.com/paper/2512.00872/) (2025)
- [Foundation Models in Biomedical Imaging: Turning Hype into Reality](https://mlsanity.com/paper/2512.15808/) (2025)
- [The potential role of AI agents in transforming nuclear medicine research and cancer management in India](https://mlsanity.com/paper/2503.07673/) (2025)
- [Virtual-Eyes: Quantitative Validation of a Lung CT Quality-Control Pipeline for Foundation-Model Cancer Risk Prediction](https://mlsanity.com/paper/2512.24294/) (2025)
- [Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer](https://mlsanity.com/paper/2601.10386/) (2026)
- [JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift](https://mlsanity.com/paper/2605.13813/) (2026)
- [When Do Cheap Probes Predict Expensive Training? Probing 3D-CT Encoders for Text Generation](https://mlsanity.com/paper/2607.22771/) (2026)
- [Anatomy Contextualized Adaptation of CT Foundation Models](https://mlsanity.com/paper/2607.27154/) (2026)
- [Same Branches, Different Trees: A Bifurcation Connectedness Metric for Coronary Artery Segmentation and FFR-CT Decision Agreement](https://mlsanity.com/paper/2607.28327/) (2026)
- [Big, Bright, or Invisible: A Frozen-Feature Benchmark of 3D CT Foundation Models](https://mlsanity.com/paper/2608.05960/) (2026)
- [Resolution Meets Reduction: Efficient Visual Context for 3D Radiology Report Generation](https://mlsanity.com/paper/2608.08713/) (2026)
- [Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke](https://mlsanity.com/paper/2507.03046/) (2025)
- [Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning](https://mlsanity.com/paper/2509.10784/) (2025)
- [3D CT-Based Coronary Calcium Assessment: A Feature-Driven Machine Learning Framework](https://mlsanity.com/paper/2510.25347/) (2025)
- [DBT-DINO: Towards Foundation model based analysis of Digital Breast Tomosynthesis](https://mlsanity.com/paper/2512.13608/) (2025)
- [EvalBlocks: A Modular Pipeline for Rapidly Evaluating Foundation Models in Medical Imaging](https://mlsanity.com/paper/2601.03811/) (2026)
- [Robust Multicentre Detection and Classification of Colorectal Liver Metastases on CT: Application of Foundation Models](https://mlsanity.com/paper/2601.07585/) (2026)

## Abstract (arXiv)

Foundation models (FMs) have shown transformative potential in radiology by performing diverse, complex tasks across imaging modalities. Here, we developed CT-FM, a large-scale 3D image-based pre-trained model designed explicitly for various radiological tasks. CT-FM was pre-trained using 148,000 computed tomography (CT) scans from the Imaging Data Commons through label-agnostic contrastive learning. We evaluated CT-FM across four categories of tasks, namely, whole-body and tumor segmentation, head CT triage, medical image retrieval, and semantic understanding, showing superior performance against state-of-the-art models. Beyond quantitative success, CT-FM demonstrated the ability to cluster regions anatomically and identify similar anatomical and structural concepts across scans. Furthermore, it remained robust across test-retest settings and indicated reasonable salient regions attached to its embeddings. This study demonstrates the value of large-scale medical imaging foundation models and by open-sourcing the model weights, code, and data, aims to support more adaptable, reliable, and interpretable AI solutions in radiology.