This title offers a reference for readers hoping to combine computer vision and machine learning. Contributors offer diverse perspectives drawn from an international pool. The book shows the development of algorithms and architectures for healthcare. Explainable AI opens ML up to show the reasons behind the decisions taken by automated algorithms, bridging a gap in meaning between those designing the technology and those implementing it in healthcare. Three sections present the role of computer vision and ML for preprocessing, application of ML to diseases and the role of explainability and interoperability of ML in healthcare. This book will be a valuable reference to medical practitioners, researchers and students interested in understanding and applying computer vision and ML in the healthcare sector.

1. Human-AI Relationships in Healthcare; 2. Deep Learning in Medical Image Analysis; 3. An Overview of Functional Near-Infrared Spectroscopy (fNIRS) and Explainable Artificial Intelligence (XAI) in fNIRS; 4. An Explainable Method for Image Registration with Applications in Medical Imaging 5. State-of-the-art deep learning method and its explainability for computerized tomography image segmentation 6. Interpretability of Segmentation and Overall Survival for Brain Tumors 7. Identification of MR image biomarkers in brain tumour patients using machine learning and radiomics features; 8. Explainable Artificial Intelligence (XAI) in Breast Cancer Identification ; 9. Interpretability of Self-Supervised Learning for Breast Cancer Image Analysis; 10. Predictive Analytics in Hospital Readmission for Diabetes Risk Patients; 11. Continuous Blood Glucose Monitoring using Explainable AI Techniques; 12. Decision Support System for Facial Emotion-based progression detection of Parkinson's Patients; 13. Interpretable Machine Learning in Athletics for Injury Risk Prediction; 14. Federated learning and Explainable AI in Healthcare;