mlsanityData to 14 Oct 2026

Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformers

arXiv 2606.19460 ↗First posted 17 Jun 2026cs.CV cs.AI cs.LGcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 64 of its 84 references that are in the map. First posted on arXiv 17 Jun 2026.

Builds on

64 of 84 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025PRISM: High-Resolution & Precise Counterfactual Medical Image Generation using Language-guided Stable Diffusion28 Feb
  2. 2023Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research15 Nov
  3. 2023Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis20 Mar

Other references · 61

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38 more
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50 most cited of 61 All 61 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Subgroup performance analysis of adaptation strategies for chest X-ray foundation modelscited by 0
  2. 2026FleXray: Universal Clinical X-ray Segmentationcited by 0

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