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Automated Quantification of Blood Flow Velocity from Time-Resolved CT Angiography.

, , , , , , и . CVII-STENT/LABELS@MICCAI, том 11043 из Lecture Notes in Computer Science, стр. 11-18. Springer, (2018)

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Automated Quantification of Blood Flow Velocity from Time-Resolved CT Angiography., , , , , , и . CVII-STENT/LABELS@MICCAI, том 11043 из Lecture Notes in Computer Science, стр. 11-18. Springer, (2018)Respiratory Motion Estimation from Cone-Beam Projections Using a Prior Model., , , и . MICCAI (1), том 5762 из Lecture Notes in Computer Science, стр. 365-372. Springer, (2009)The pythagorean averages as group images in efficient groupwise registration., , , , и . ISBI, стр. 1261-1264. IEEE, (2016)Artificially augmenting data or adding more samples? A study on a 3D CNN for lung nodule classification., , и . Medical Imaging: Computer-Aided Diagnosis, том 11314 из SPIE Proceedings, SPIE, (2020)Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging., , , , , , , , , и 30 other автор(ы). CoRR, (2020)COVID-19 Lesion Segmentation Framework for the Contrast-Enhanced CT in the Absence of Contrast-Enhanced CT Annotations., , , и . MILLanD@MICCAI, том 14307 из Lecture Notes in Computer Science, стр. 71-81. Springer, (2023)Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge., , , , , , , , , и 21 other автор(ы). CoRR, (2016)Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients., , , , , , , , , и 12 other автор(ы). CoRR, (2020)Intensity Standardization of Skeleton in Follow-Up Whole-Body MRI., , , , , и . CSI@MICCAI, том 11397 из Lecture Notes in Computer Science, стр. 77-89. Springer, (2018)Handcrafted Features Can Boost Performance and Data-Efficiency for Deep Detection of Lung Nodules From CT Imaging., , , и . IEEE Access, (2023)