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Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimization for Multi-Modal Cardiac Image Segmentation., , , , and . IEEE Trans. Medical Imaging, 40 (7): 1838-1851 (2021)Automatic 3D+t four-chamber CMR quantification of the UK biobank: integrating imaging and non-imaging data priors at scale., , , , , , , , and . Medical Image Anal., (2022)Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data., , , , and . Medical Image Anal., (2019)Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images., , , , and . CoRR, (2021)A Deep Discontinuity-Preserving Image Registration Network., , , and . MICCAI (4), volume 12904 of Lecture Notes in Computer Science, page 46-55. Springer, (2021)Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation., , , and . CVPR Workshops, page 2897-2904. IEEE, (2022)Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge., , , , , , , , , and 13 other author(s). CoRR, (2020)Learned Local Attention Maps for Synthesising Vessel Segmentations from T2 MRI., , , , , , , and . SASHIMI@MICCAI, volume 14288 of Lecture Notes in Computer Science, page 32-41. Springer, (2023)Robust group-wise rigid registration of point sets using t-mixture model., , , and . Medical Imaging: Image Processing, volume 9784 of SPIE Proceedings, page 97840S. SPIE, (2016)Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability., , , , , , and . Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 10952 of SPIE Proceedings, page 109520O. SPIE, (2019)