Textbook in PDF format

This book combines technology and the medical domain. It covers advances in Computer Vision (CV) and Machine Learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in Computer Science, electronics and communications, information technology, instrumentation and control, and electrical engineering.
Preface
Human–AI Relationship in Healthcare
Deep Learning in Medical Image Analysis: Recent Models and Explainability
An Overview of Functional Near-Infrared Spectroscopy and Explainable Artificial Intelligence in fNIRS
An Explainable Method for Image Registration with Applications in Medical Imaging
State-of-the-Art Deep Learning Method and Its Explainability for Computerized Tomography Image Segmentation
Interpretability of Segmentation and Overall Survival for Brain Tumors
Identification of MR Image Biomarkers in Brain Tumor Patients Using Machine Learning and Radiomics Features
Explainable Artificial Intelligence in Breast Cancer Identification
Interpretability of Self-Supervised Learning for Breast Cancer Image Analysis
Predictive Analytics in Hospital Readmission for Diabetes Risk Patients
Continuous Blood Glucose Monitoring Using Explainable AI Techniques
Decision Support System for Facial Emotion-Based Progression Detection of Parkinson’s Patients
Interpretable Machine Learning in Athletics for Injury Risk Prediction
Federated Learning and Explainable AI in Healthcare