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Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation.

, , , , , , , and . CoRR, (2019)

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How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning., , and . MICCAI (6), volume 11769 of Lecture Notes in Computer Science, page 649-657. Springer, (2019)When Does Bone Suppression And Lung Field Segmentation Improve Chest X-Ray Disease Classification?, , , , , , , , and . ISBI, page 1362-1366. IEEE, (2019)Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans., , and . ISBI, page 91-95. IEEE, (2021)Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans., , and . CoRR, (2020)Gaussian Processes for Machine Learning (GPML) Toolbox., and . J. Mach. Learn. Res., (2010)Additive Gaussian Processes., , and . NIPS, page 226-234. (2011)glm-ie: Generalised Linear Models Inference & Estimation Toolbox.. J. Mach. Learn. Res., (2012)Learning metal artifact reduction in cardiac CT images with moving pacemakers., , , , and . Medical Image Anal., (2020)Approximations for Binary Gaussian Process Classification, and . Journal of Machine Learning Research, (October 2008)Delineation of coronary stents in intravascular ultrasound pullbacks., , , , , , , , and . Medical Imaging: Image-Guided Procedures, volume 11598 of SPIE Proceedings, SPIE, (2021)