Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

<p>Uncertainty Modelling.- MOrphologically-aware Jaccard-based ITerative Optimization (MOJITO) for Consensus Segmentation.- Quantification of Predictive Uncertainty via Inference-Time Sampling.- Uncertainty categories in medical image segmentation: a study of source-related diversity..- On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation.- What Do Untargeted Adversarial Examples Reveal In Medical Image Segmentation?..- Uncertainty calibration.- Improved post-hoc probability calibration for out-of-domain MRI segmentation..- Improving error detection in deep learning-based radiotherapy autocontouring using Bayesian uncertainty.- A Plug-and-Play Method to Compute Uncertainty.- Calibration of Deep Medical Image Classifiers: An Empirical Comparison using Dermatology and Histopathology Datasets.- Annotation uncertainty and out of distribution management.- nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods.- Generalized Probabilistic U-Net for medical image segmentation.- Joint paraspinal muscle segmentation and inter-rater labeling variability prediction with multi-task TransUNet.- Information Gain Sampling for Active Learning in Medical Image Classification.</p>