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Deep-learning-based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography., , , and . Medical Imaging: Image-Guided Procedures, volume 10951 of SPIE Proceedings, page 109510R. SPIE, (2019)Semantic denoising autoencoders for retinal optical coherence tomography., , , and . CoRR, (2019)Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say "I don't know" for Ambiguous Cases., , and . CoRR, (2019)Calibration of Model Uncertainty for Dropout Variational Inference., , , and . CoRR, (2020)Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer., , and . CoRR, (2020)Retinal OCT disease classification with variational autoencoder regularization., , , and . CoRR, (2019)A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000., , and . CoRR, (2023)Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference., , , and . CoRR, (2019)A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000., , and . MICCAI (1), volume 13431 of Lecture Notes in Computer Science, page 654-663. Springer, (2022)Relative Pose Consistency for Semi-Supervised Head Pose Estimation., , and . FG, page 1-8. IEEE, (2021)