Uncertainty-guided Generation of Dark-field Radiographs
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908781503315968 |
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| author | Felsner, Lina Bast, Henriette Dorosti, Tina Schaff, Florian Pfeiffer, Franz Pfeiffer, Daniela Schnabel, Julia |
| author_facet | Felsner, Lina Bast, Henriette Dorosti, Tina Schaff, Florian Pfeiffer, Franz Pfeiffer, Daniela Schnabel, Julia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_15859 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Uncertainty-guided Generation of Dark-field Radiographs Felsner, Lina Bast, Henriette Dorosti, Tina Schaff, Florian Pfeiffer, Franz Pfeiffer, Daniela Schnabel, Julia Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Uncertainty-guided Generation of Dark-field Radiographs |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.15859 |