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Semi-automatic Cardiac and Respiratory Gated MRI for Cardiac Assessment During Exercise.

, , , , , , , , , , and . CMMI/RAMBO/SWITCH@MICCAI, volume 10555 of Lecture Notes in Computer Science, page 86-95. Springer, (2017)

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Uncertainty aware training to improve deep learning model calibration for classification of cardiac MR images., , , , , , , , , and 2 other author(s). Medical Image Anal., (August 2023)Improved AI-Based Segmentation of Apical and Basal Slices from Clinical Cine CMR., , , , , and . STACOM@MICCAI, volume 13131 of Lecture Notes in Computer Science, page 84-92. Springer, (2021)Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation., , , , , , and . MICCAI (3), volume 12903 of Lecture Notes in Computer Science, page 413-423. Springer, (2021)Automatic Detection of Extra-Cardiac Findings in Cardiovascular Magnetic Resonance., , , , and . MIUA, volume 12722 of Lecture Notes in Computer Science, page 98-107. Springer, (2021)Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors., , , , , , , and . ShapeMI@MICCAI, volume 11167 of Lecture Notes in Computer Science, page 300-310. Springer, (2018)High-quality segmentation of low quality cardiac MR images using k-space artefact correction., , , , , , , , and . MIDL, volume 102 of Proceedings of Machine Learning Research, page 380-389. PMLR, (2019)Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning., , , , , , , , , and 1 other author(s). Medical Image Anal., (2019)Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views., , , , and . CoRR, (2023)A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis., , , , , , , , , and 3 other author(s). CoRR, (2022)Regional Multi-View Learning for Cardiac Motion Analysis: Application to Identification of Dilated Cardiomyopathy Patients., , , , , , , , and . IEEE Trans. Biomed. Eng., 66 (4): 956-966 (2019)