Explainable AI in Healthcare

图书简介

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.This book will benefit readers in the following ways:Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health careInvestigates bridges between computer scientists and physicians being built with XAIFocuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparentInitiates discussions on human-AI relationships in health careUnites learning for privacy preservation in health care

1. Human–AI Relationship in Healthcare Mukta Joshi, Nicola Pezzotti, and Jacob T. Browne 2. Deep Learning in Medical Image Analysis: Recent Models and Explainability Swati Rai, Jignesh S. Bhatt, and Sarat Kumar Patra 3. An Overview of Functional Near-Infrared Spectroscopy and Explainable Artificial Intelligence in fNIRS N. Sertac Artan 4. An Explainable Method for Image Registration with Applications in Medical Imaging Srikrishnan Divakaran 5. State-of-the-Art Deep Learning Method and Its Explainability for Computerized Tomography Image Segmentation Wing Keung Cheung 6. Interpretability of Segmentation and Overall Survival for Brain Tumors Rupal Kapdi, Snehal Rajput, Mohendra Roy, and Mehul S Raval 7. Identification of MR Image Biomarkers in Brain Tumor Patients Using Machine Learning and Radiomics Features Jayendra M. Bhalodiya 8. Explainable Artificial Intelligence in Breast Cancer Identification Pooja Bidwai, Smita Khairnar, and Shilpa Gite 9. Interpretability of Self-Supervised Learning for Breast Cancer Image Analysis Gitika Jha, Manashree Jhawar, Vedant Manelkar, Radhika Kotecha, Ashish Phophalia, and Komal Borisagar 10. Predictive Analytics in Hospital Readmission for Diabetes Risk Patients Kaustubh V. Sakhare, Vibha Vyas, and Mousami Munot 11. Continuous Blood Glucose Monitoring Using Explainable AI Techniques Ketan K. Lad and Maulin Joshi 12. Decision Support System for Facial Emotion-Based Progression Detection of Parkinson’s Patients Bhakti Sonawane and Priyanka Sharma 13. Interpretable Machine Learning in Athletics for Injury Risk Prediction Srishti Sharma, Mehul S Raval, Tolga Kaya, and Srikrishnan Divakaran 14. Federated Learning and Explainable AI in Heal

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Explainable AI in Healthcare（Analytics and AI for Healthcare） 医疗领域可解释的人工智能：用于生物医学的拆箱机器学习 生物医学工程学

Explainable AI in Healthcare（Analytics and AI for Healthcare）

医疗领域可解释的人工智能：用于生物医学的拆箱机器学习
生物医学工程学