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Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability.

, , , , , , и . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, том 10952 из SPIE Proceedings, стр. 109520O. SPIE, (2019)

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Ludwig Ritschl

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Towards Full-body X-ray Images., , , и . Bildverarbeitung für die Medizin, стр. 86-91. Springer Vieweg, (2018)Abstract: Realistic Collimated X-ray Image Simulation Pipeline., , , , , , и . Bildverarbeitung für die Medizin, стр. 218. Springer, (2024)Moiré artefact reduction in Talbot-Lau X-ray imaging., , , , , , , и . ISBI, стр. 57-60. IEEE, (2018)Deep Learning-Based Denoising of Mammographic Images Using Physics-Driven Data Augmentation., , , , и . Bildverarbeitung für die Medizin, стр. 94-100. Springer, (2020)Combining 2-D and 3-D Weight-Bearing X-Ray Images., , , , , и . Bildverarbeitung für die Medizin, стр. 335-340. Springer, (2020)Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks., , , , , , и . CoRR, (2018)A hybrid approach for virtual clinical trials for mammographic imaging., , , , и . IWBI, том 10718 из SPIE Proceedings, стр. 107180Z. SPIE, (2018)Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation., , , , и . CoRR, (2019)A Realistic Collimated X-Ray Image Simulation Pipeline., , , , , , и . DALI@MICCAI, том 14379 из Lecture Notes in Computer Science, стр. 137-145. Springer, (2023)Lesion Ground Truth Estimation for a Physical Breast Phantom., , , , , и . Bildverarbeitung für die Medizin, стр. 243-248. Springer, (2017)