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A CDMAM Image Phantom Software Improvement for Human Observer Assessment.

, , и . Digital Mammography / IWDM, том 5116 из Lecture Notes in Computer Science, стр. 181-187. Springer, (2008)

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Pauline Chevalier

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Automatic scoring of CDMAM using a model of the recognition threshold of the human visual system: R., , и . ICIP, стр. 2489-2492. IEEE, (2009)Pectoral muscle segmentation in breast tomosynthesis with deep learning., , , , , , и . Medical Imaging: Computer-Aided Diagnosis, том 10575 из SPIE Proceedings, стр. 105752J. SPIE, (2018)Phantom-based comparison of microcalcification visibility between digital and synthetic mammography using humans and a deep neural network as observers., , , и . IWBI, том 10718 из SPIE Proceedings, стр. 1071810. SPIE, (2018)Influence of Geometrical Factors on Phase Contrast Fiber Images., , , , и . Digital Mammography / IWDM, том 6136 из Lecture Notes in Computer Science, стр. 334-341. Springer, (2010)Digital breast tomosynthesis: impact of a new beam quality on dose to patients., , , , и . IWBI, том 10718 из SPIE Proceedings, стр. 107181M. SPIE, (2018)Digital Breast Tomosynthesis: Image Quality and Dose Saving of the Synthesized Image., , , , и . Digital Mammography / IWDM, том 8539 из Lecture Notes in Computer Science, стр. 150-157. Springer, (2014)A CDMAM Image Phantom Software Improvement for Human Observer Assessment., , и . Digital Mammography / IWDM, том 5116 из Lecture Notes in Computer Science, стр. 181-187. Springer, (2008)