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Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions.

, , , and . CoRR, (2022)

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Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context., , , , and . CoRR, (2023)Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions., , , and . CoRR, (2022)Assessing regularization in tomographic imaging via hallucinations in the null space., , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 11599 of SPIE Proceedings, SPIE, (2021)Progressively-Growing AmbientGANs for learning stochastic object models from imaging measurements., , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 11316 of SPIE Proceedings, page 113160Q. SPIE, (2020)Quantification of heterogeneity observed in medical images., and . BMC Medical Imaging, (2013)Learning stochastic object model from noisy imaging measurements using AmbientGANs., , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 10952 of SPIE Proceedings, page 109520M. SPIE, (2019)On hallucinations in tomographic image reconstruction., , , and . CoRR, (2020)A Method for Evaluating the Capacity of Generative Adversarial Networks to Reproduce High-order Spatial Context., , and . CoRR, (2021)Evaluating the capacity of deep generative models to reproduce measurable high-order spatial arrangements in diagnostic images., , and . Medical Imaging: Image Processing, volume 12032 of SPIE Proceedings, SPIE, (2022)Evaluating generative stochastic image models using task-based image quality measures., , , , , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 12467 of SPIE Proceedings, SPIE, (2023)