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Capturing Single-Cell Phenotypic Variation via Unsupervised Representation Learning., , , , , and . MIDL, volume 102 of Proceedings of Machine Learning Research, page 315-325. PMLR, (2019)The truth is hard to make: Validation of medical image registration., , , and . ICPR, page 2294-2300. IEEE, (2016)Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation., , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 109491V. SPIE, (2019)Supervised quality assessment of medical image registration: Application to intra-patient CT lung registration., , , and . Medical Image Anal., 16 (8): 1521-1531 (2012)Quantifying Graft Detachment after Descemet's Membrane Endothelial Keratoplasty with Deep Convolutional Neural Networks., , , , and . CoRR, (2020)Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge., , , , , , , , , and 9 other author(s). Medical Image Anal., (2019)Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors., , , , , , , , , and 2 other author(s). CoRR, (2020)Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases., , , , , and . Int. J. Comput. Assist. Radiol. Surg., 8 (6): 929-936 (2013)LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset., , , , , , , , , and 11 other author(s). IEEE J. Biomed. Health Informatics, 28 (3): 1161-1172 (March 2024)A patient specific 4D MRI liver motion model based on sparse imaging and registration., , , and . Medical Imaging: Image-Guided Procedures, volume 8671 of SPIE Proceedings, page 86710S. SPIE, (2013)