Uncertainty-guided Generation of Dark-field Radiographs

2026-01-22

AI Summary

This work addresses the challenge of training deep learning models for dark-field X-ray imaging due to the scarcity of real dark-field data by proposing an uncertainty-guided progressive generative adversarial network. For the first time, it enables high-quality synthesis of dark-field images from conventional chest radiographs. The method integrates aleatoric and epistemic uncertainties within an image-to-image translation framework, leveraging Bayesian deep learning and small-angle scattering modeling to progressively enhance structural fidelity. Experimental results demonstrate that the generated images exhibit stage-wise improvements in quantitative metrics and achieve strong generalization on out-of-distribution data. This approach significantly improves model reliability, interpretability, and clinical applicability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

X-ray dark-field radiography provides complementary diagnostic information to conventional attenuation imaging by visualizing microstructural tissue changes through small-angle scattering. However, the limited availability of such data poses challenges for developing robust deep learning models. In this work, we present the first framework for generating dark-field images directly from standard attenuation chest X-rays using an Uncertainty-Guided Progressive Generative Adversarial Network. The model incorporates both aleatoric and epistemic uncertainty to improve interpretability and reliability. Experiments demonstrate high structural fidelity of the generated images, with consistent improvement of quantitative metrics across stages. Furthermore, out-of-distribution evaluation confirms that the proposed model generalizes well. Our results indicate that uncertainty-guided generative modeling enables realistic dark-field image synthesis and provides a reliable foundation for future clinical applications.

Problem

• dark-field radiography
• data scarcity
• image generation
• deep learning
• uncertainty

Innovation

• Uncertainty-Guided Generation
• Dark-field Radiography
• Progressive GAN
• Aleatoric Uncertainty
• Epistemic Uncertainty