Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Chapter 1 MOrphologically-Aware Jaccard-Based ITerative Optimization (MOJITO) for Consensus Segmentation

Chapter 2 Quantification of Predictive Uncertainty via Inference-Time Sampling

Chapter 3 How certain are your uncertainties?

Chapter 5 What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?

Chapter 6 Improved Post-hoc Probability Calibration for Out-of-Domain MRI Segmentation

Chapter 7 Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertainty

Chapter 8 Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty

Chapter 9 Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopathology Datasets

Chapter 10 nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods

Chapter 11 Generalized Probabilistic U-Net for Medical Image Segementation

Chapter 12 Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-task TransUNet

Chapter 13 Information Gain Sampling for Active Learning in Medical Image Classification