Article

Generalizable CT Vision-Language Modeling for Population Health and Disease Risk

2025-07-03

Abstract excerpt

Vision-language foundation models (VLMs) for computed tomography (CT) are emerging tools that learn generalizable representations from large-scale clinical imaging data. While these models can predict task-specific labels, the extent to which their representations capture the clinical, physiological, and longitudinal variation of real-world patient populations remains unclear. We introduce Percival, a CT-native VL...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: dad7d57f-31de-5909-ba96-52b80427d6db
DOI: 10.1101/2025.07.03.25330654

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Generalizable CT Vision-Language Modeling for Population Health and Disease RiskDOI 10.1101/2025.07.03.25330654Read

Health system learning achieves generalist neuroimaging modelsSemantic proximity Read

Merlin: A Vision Language Foundation Model for 3D Computed TomographySemantic proximity Read

Vision-Language Foundation Models Do Not Transfer to Medical Imaging Classification: A Negative Result on Chest X-ray DiagnosisSemantic proximity Read

Pillar-0: A New Frontier for Radiology Foundation ModelsSemantic proximity Read

Foundation model embeddings for quantitative tumor imaging biomarkersSemantic proximity Read

SLIViT: a general AI framework for clinical-feature diagnosis from limited 3D biomedical-imaging dataSemantic proximity Read

Integrating Multimodal Data with Large Foundation Models in HealthcareSemantic proximity Read

Advances in Deep Learning for Medical Imaging: Foundations, Evolution, and ImpactSemantic proximity Read

Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic DataSemantic proximity Read

Evaluating Vision and Pathology Foundation Models for Computational Pathology: A Comprehensive Benchmark StudySemantic proximity Read

Leveraging a Vision-Language Model with Natural Text Supervision for MRI Retrieval, Captioning, Classification, and Visual Question AnsweringSemantic proximity Read

Evaluating Vision and Pathology Foundation Models for Computational Pathology: A Comprehensive Benchmark StudySemantic proximity Read