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 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 12 Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences

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