A Generative Foundation Model for Chest Radiography

• URL: http://arxiv.org/abs/2509.03903v1
• Date: Thu, 04 Sep 2025 05:53:58 GMT
• Title: A Generative Foundation Model for Chest Radiography
• Authors: Yuanfeng Ji, Dan Lin, Xiyue Wang, Lu Zhang, Wenhui Zhou, Chongjian Ge, Ruihang Chu, Xiaoli Yang, Junhan Zhao, Junsong Chen, Xiangde Luo, Sen Yang, Jin Fang, Ping Luo, Ruijiang Li,
• Abstract summary: ChexGen is a generative vision-language foundation model that introduces a unified framework for text-, mask-, and bounding box-guided synthesis of chest radiographs.<n>ChexGen was pretrained on the largest curated chest X-ray dataset to date, consisting of 960,000 radiograph-report pairs.
• Score: 43.32039823252467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scarcity of well-annotated diverse medical images is a major hurdle for developing reliable AI models in healthcare. Substantial technical advances have been made in generative foundation models for natural images. Here we develop `ChexGen', a generative vision-language foundation model that introduces a unified framework for text-, mask-, and bounding box-guided synthesis of chest radiographs. Built upon the latent diffusion transformer architecture, ChexGen was pretrained on the largest curated chest X-ray dataset to date, consisting of 960,000 radiograph-report pairs. ChexGen achieves accurate synthesis of radiographs through expert evaluations and quantitative metrics. We demonstrate the utility of ChexGen for training data augmentation and supervised pretraining, which led to performance improvements across disease classification, detection, and segmentation tasks using a small fraction of training data. Further, our model enables the creation of diverse patient cohorts that enhance model fairness by detecting and mitigating demographic biases. Our study supports the transformative role of generative foundation models in building more accurate, data-efficient, and equitable medical AI systems.

Related papers

• X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data [86.52299247918637]
  Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges.<n>Despite the recent strides in diffusion-based methods for enhancing the representation of tailed lesions, the paucity of rare lesion exemplars curtails the generative capabilities of these approaches.<n>We propose a novel data synthesis pipeline designed to augment tail lesions utilizing a copious supply of conventional normal X-rays.
  arXiv  Detail & Related papers  (2025-12-24T06:14:55Z)
• X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography [89.84588038174721]
  Computed Tomography serves as an indispensable tool in clinical, providing non-invasive visualization of internal anatomical structures.<n>Existing CT reconstruction works are limited to small-capacity model architecture and inflexible volume representation.<n>We present X-GRM, a large feedforward model for reconstructing 3D CT volumes from sparse-view 2D X-ray projections.
  arXiv  Detail & Related papers  (2025-05-21T08:14:10Z)
• Pre-training on High Definition X-ray Images: An Experimental Study [19.46094537296955]
  We propose the first high-definition (1280 $times$ 1280) X-ray based pre-trained foundation vision model on a large-scale dataset. Our model follows the masked auto-encoder framework which takes the tokens after mask processing (with a high rate) is used as input. We validate the effectiveness of our model on two downstream tasks, including X-ray report generation and disease recognition.
  arXiv  Detail & Related papers  (2024-04-27T14:29:53Z)
• Evaluating the feasibility of using Generative Models to generate Chest X-Ray Data [0.0]
  We explore the feasibility of using generative models to generate synthetic chest X-ray images for medical diagnosis purposes. We utilised the Chest X-ray 14 dataset for our experiments and evaluated the performance of our models. Our results show that the generated images are visually convincing and can be used to improve the accuracy of classification models.
  arXiv  Detail & Related papers  (2023-05-30T10:36:30Z)
• Vision-Language Generative Model for View-Specific Chest X-ray Generation [18.347723213970696]
  ViewXGen is designed to overcome the limitations of existing methods to generate frontal-view chest X-rays. Our approach takes into consideration the diverse view positions found in the dataset, enabling the generation of chest X-rays with specific views.
  arXiv  Detail & Related papers  (2023-02-23T17:13:25Z)
• RoentGen: Vision-Language Foundation Model for Chest X-ray Generation [7.618389245539657]
  We develop a strategy to overcome the large natural-medical distributional shift by adapting a pre-trained latent diffusion model on a corpus of publicly available chest x-rays. We investigate the model's ability to generate high-fidelity, diverse synthetic CXR conditioned on text prompts. We present evidence that the resulting model (RoentGen) is able to create visually convincing, diverse synthetic CXR images.
  arXiv  Detail & Related papers  (2022-11-23T06:58:09Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets [55.06983249986729]
  We show that a deep learning model performing well when tested on the same dataset as training data starts to perform poorly when it is tested on a dataset from a different source. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation.
  arXiv  Detail & Related papers  (2020-08-04T07:41:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.