Vision Foundation Models for Computed Tomography

Suraj Pai; Ibrahim Hadzic; Dennis Bontempi; Keno Bressem; Benjamin H. Kann; Andriy Fedorov; Raymond H. Mak; Hugo J. W. L. Aerts

arXiv · 2501.09001

Vision Foundation Models for Computed Tomography

Suraj Pai·Ibrahim Hadzic·Dennis Bontempi·Keno Bressem·Benjamin H. Kann·Andriy Fedorov·Raymond H. Mak·Hugo J. W. L. Aerts

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Abstract

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.

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Suraj Pai, Ibrahim Hadzic, Dennis Bontempi, Keno Bressem, Benjamin H. Kann, Andriy Fedorov, Raymond H. Mak, Hugo J. W. L. Aerts. 2025-01-15. Vision Foundation Models for Computed Tomography. https://arxiv.org/abs/2501.09001

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