Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

Chapter 1 Image Registration via Stochastic Gradient Markov Chain Monte Carlo

Chapter 2 RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation

Chapter 3 Hierarchical Brain Parcellation with Uncertainty

Chapter 4 Quantitative Comparison of Monte-Carlo Dropout Uncertainty Measures for Multi-class Segmentation

Chapter 5 Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps

Chapter 6 Weight Averaging Impact on the Uncertainty of Retinal Artery-Venous Segmentation

Chapter 7 Improving Pathological Distribution Measurements with Bayesian Uncertainty

Chapter 8 Improving Reliability of Clinical Models Using Prediction Calibration

Chapter 9 Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

Chapter 10 Uncertainty Estimation for Assessment of 3D US Scan Adequacy and DDH Metric Reliability

Chapter 11 Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates

Chapter 13 Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders

Chapter 14 Multi-scale Profiling of Brain Multigraphs by Eigen-Based Cross-diffusion and Heat Tracing for Brain State Profiling

Chapter 15 Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation

Chapter 16 Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth

Chapter 17 Geometric Deep Learning for Post-Menstrual Age Prediction Based on the Neonatal White Matter Cortical Surface

Chapter 18 The GraphNet Zoo: An All-in-One Graph Based Deep Semi-supervised Framework for Medical Image Classification

Chapter 19 Intraoperative Liver Surface Completion with Graph Convolutional VAE

Chapter 20 HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification