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Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation., , , , and . CoRR, (2019)Towards cartilage diagnosis in X-ray phase-contrast interferometry., , , , , , and . ISBI, page 573-577. IEEE, (2017)Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks., , , , , , and . CoRR, (2018)A hybrid approach for virtual clinical trials for mammographic imaging., , , , and . IWBI, volume 10718 of SPIE Proceedings, page 107180Z. SPIE, (2018)Moiré artefact reduction in Talbot-Lau X-ray imaging., , , , , , , and . ISBI, page 57-60. IEEE, (2018)Volumetric breast density measurement for personalized screening: accuracy, reproducibility, and agreement with visual assessment., , , , , , , and . IWBI, volume 10718 of SPIE Proceedings, page 107180A. SPIE, (2018)Towards an analytic model describing the effect of scan angle and slice thickness on the in-plane spatial resolution of calcifications in digital breast tomosynthesis., , , , , and . IWBI, volume 10718 of SPIE Proceedings, page 107181R. SPIE, (2018)Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability., , , , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 10952 of SPIE Proceedings, page 109520O. SPIE, (2019)