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Felsner, L. ; Bast, H.* ; Dorosti, T.* ; Schaff, F.* ; Pfeiffer, F.* ; Pfeiffer, D.* ; Schnabel, J.A.

Uncertainty-Guided Generation of Dark-Field Radiographs.

In: (23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026, 8-11 April 2026, London). 2026. (Proceedings International Symposium on Biomedical Imaging ; 2026-April)
DOI
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.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Generative Adversarial Networks ; Uncertainty Modeling ; X-ray Dark-field Image Generation
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Konferenztitel 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026
Konferzenzdatum 8-11 April 2026
Konferenzort London
Quellenangaben Band: 2026-April Heft: , Seiten: , Artikelnummer: , Supplement: ,
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)