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Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction.

, , , , , , , , and . MICCAI (2), volume 11765 of Lecture Notes in Computer Science, page 541-549. Springer, (2019)

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Better Together: Data Harmonization and Cross-Study Analysis of Abdominal MRI Data From UK Biobank and the German National Cohort, , , , , , , , , and 15 other author(s). Investigative Radiology, (2022)Deep Learning for Cardiac Image Segmentation: A Review, , , , , , and . Front. Cardiovasc. Med., (March 2020)CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy., , , , , , and . IEEE Trans. Medical Imaging, 43 (3): 1259-1269 (March 2024)Multi-atlas segmentation with augmented features for cardiac MR images., , , and . Medical Image Anal., 19 (1): 98-109 (2015)Efficient Deep Representation Learning by Adaptive Latent Space Sampling., , , , , , and . CoRR, (2020)Tracking of Migrating Glioma Cells in Feature Space., , , , and . ISBI, page 272-275. IEEE, (2007)Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images., , , and . MIDL, volume 172 of Proceedings of Machine Learning Research, page 1322-1335. PMLR, (2022)Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning., , , , , , , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 259-267. Springer, (2018)Combining Deep Learning and Shape Priors for Bi-Ventricular Segmentation of Volumetric Cardiac Magnetic Resonance Images., , , , , , , , , and . ShapeMI@MICCAI, volume 11167 of Lecture Notes in Computer Science, page 258-267. Springer, (2018)Three-dimensional cardiovascular imaging-genetics: a mass univariate framework., , , , , , , , , and 3 other author(s). Bioinform., 34 (1): 97-103 (2018)