2026-06-19原文2 min readUpdated: 2026-06-19

Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers

Researchers introduce the first billion-parameter generative foundation model for chest radiograph synthesis, with over 1.3B parameters trained on 1.2M radiographs and expert metadata. The model supports controllable generation across demographics, views, and pathologies, achieving near-clinical indistinguishability.

SourcearXiv Computer VisionAuthor: Fabio De Sousa Ribeiro, Emma A. M. Stanley, Charles Jones, Tian Xia, Dominic C. Marshall, Laurent Renard Trich\'e, Christopher V. Cosgriff, Panagiotis Dimitrakopoulos, Sotirios A. Tsaftaris, Ben Glocker

[2606.19460] Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers

[Submitted on 17 Jun 2026]

Title:Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers

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Abstract:We introduce the first generative foundation model for chest radiograph synthesis trained from scratch at the billion-parameter scale. Existing radiographic AI models often suffer from poor generalisation across patient subpopulations, institutions, and acquisition settings, resulting in limited real-world clinical utility. Controlled, high-fidelity synthesis of chest radiographs is a promising path toward diversifying clinical datasets and evaluating the robustness of diagnostic models. Therefore, we present the largest specialist generative foundation model for chest radiographs to date, with over 1.3B parameters, trained for 1.6T tokens on a curated, heterogeneous dataset comprising 1.2M radiographs and clinical expert-guided metadata. Our model supports controllable radiograph generation and editing across multiple demographic subgroups, acquisition views, and a dozen pathologies. Moreover, we significantly advance the state of the art in radiograph synthesis fidelity, producing images that are indistinguishable from real radiographs to clinical experts.

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Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as: arXiv:2606.19460 [cs.CV]

(or arXiv:2606.19460v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.19460

arXiv-issued DOI via DataCite (pending registration)

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From: Fabio De Sousa Ribeiro PhD [view email] [v1] Wed, 17 Jun 2026 18:01:18 UTC (23,990 KB)

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