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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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Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning., , , , , , , , , and 1 other author(s). CoRR, (2018)Deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions., , , , , , , , , and . DeCaF/FAIR@MICCAI, volume 13573 of Lecture Notes in Computer Science, page 168-177. Springer, (2022)Deep Learning Using K-Space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection., , , , , , , , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 250-258. Springer, (2018)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)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)Synthesising Images and Labels Between MR Sequence Types with CycleGAN., , , , , , and . DART/MIL3ID@MICCAI, volume 11795 of Lecture Notes in Computer Science, page 45-53. Springer, (2019)Quality-Aware Semi-supervised Learning for CMR Segmentation., , , , , , and . M&Ms and EMIDEC/STACOM@MICCAI, volume 12592 of Lecture Notes in Computer Science, page 97-107. Springer, (2020)Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging., , , , , , , , , and 4 other author(s). STACOM@MICCAI, volume 13593 of Lecture Notes in Computer Science, page 101-111. Springer, (2022)Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation., , , , , , , , and . IEEE Trans. Medical Imaging, 39 (12): 4001-4010 (2020)