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A deep learning approach for the segmentation of myocardial diseases.

, , , , , и . ICPR, стр. 4544-4551. IEEE, (2020)

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Arnaud Boucher

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Calibration of spatial distribution of light sources in reflectance transformation imaging based on adaptive local density estimation., , , , , , , , , и . J. Electronic Imaging, 29 (4): 041004 (2020)An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net)., , , , , и . Sensors, 22 (6): 2084 (2022)Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge., , , , , , , , , и 23 other автор(ы). CoRR, (2021)Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge., , , , , , , , , и 23 other автор(ы). Medical Image Anal., (2022)Mammogram CAD, hybrid registration and iconic analysis., , и . Image Processing: Machine Vision Applications, том 8661 из SPIE Proceedings, стр. 86610J. SPIE, (2013)Efficient 3D Deep Learning for Myocardial Diseases Segmentation., , , , , и . M&Ms and EMIDEC/STACOM@MICCAI, том 12592 из Lecture Notes in Computer Science, стр. 359-368. Springer, (2020)Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets., , , , , , , , , и 3 other автор(ы). J. Imaging, 9 (6): 123 (2023)A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases., , , , , и . IPTA, стр. 1-6. IEEE, (2020)A deep learning approach for the segmentation of myocardial diseases., , , , , и . ICPR, стр. 4544-4551. IEEE, (2020)Multi-atlas Segmentation of the Aorta from 4D Flow MRI: Comparison of Several Fusion Strategies., , , , , , , , и . STACOM@MICCAI, том 13131 из Lecture Notes in Computer Science, стр. 3-11. Springer, (2021)