Understanding and Interpreting Machine Learning in Medical Image Computing Applications

Chapter 1 Alzheimer’s Disease Modelling and Staging Through Independent Gaussian Process Analysis of Spatio-Temporal Brain Changes

Chapter 2 Multi-channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer’s Disease

Chapter 3 Visualizing Convolutional Networks for MRI-Based Diagnosis of Alzheimer’s Disease

Chapter 4 Finding Effective Ways to (Machine) Learn fMRI-Based Classifiers from Multi-site Data

Chapter 5 Towards Robust CT-Ultrasound Registration Using Deep Learning Methods

Chapter 7 Evaluation of Strategies for PET Motion Correction - Manifold Learning vs. Deep Learning

Chapter 8 Exploring Adversarial Examples

Chapter 9 Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks

Chapter 10 Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks

Chapter 11 Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images

Chapter 12 Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment

Chapter 13 Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification

Chapter 14 Regression Concept Vectors for Bidirectional Explanations in Histopathology

Chapter 15 Towards Complementary Explanations Using Deep Neural Networks

Chapter 16 How Users Perceive Content-Based Image Retrieval for Identifying Skin Images