Explainable AI in Healthcare
eBook - ePub

Explainable AI in Healthcare

Unboxing Machine Learning for Biomedicine

  1. 304 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Explainable AI in Healthcare

Unboxing Machine Learning for Biomedicine

About this book

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 care

  • Investigates bridges between computer scientists and physicians being built with XAI

  • Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent

  • Initiates discussions on human-AI relationships in health care

  • Unites learning for privacy preservation in health care

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Yes, you can access Explainable AI in Healthcare by Mehul S Raval, Mohendra Roy, Tolga Kaya, Rupal Kapdi, Mehul S Raval,Mohendra Roy,Tolga Kaya,Rupal Kapdi in PDF and/or ePUB format, as well as other popular books in Computer Science & Pharmaceutical, Biotechnology & Healthcare Industry. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Series
  4. Title
  5. Copyright
  6. Dedication
  7. Contents
  8. Foreword
  9. Preface
  10. Acknowledgements
  11. Acronyms and Abbreviations
  12. Editors
  13. Contributors
  14. 1 Human–AI Relationship in Healthcare
  15. 2 Deep Learning in Medical Image Analysis: Recent Models and Explainability
  16. 3 An Overview of Functional Near-Infrared Spectroscopy and Explainable Artificial Intelligence in fNIRS
  17. 4 An Explainable Method for Image Registration with Applications in Medical Imaging
  18. 5 State-of-the-Art Deep Learning Method and Its Explainability for Computerized Tomography Image Segmentation
  19. 6 Interpretability of Segmentation and Overall Survival for Brain Tumors
  20. 7 Identification of MR Image Biomarkers in Brain Tumor Patients Using Machine Learning and Radiomics Features
  21. 8 Explainable Artificial Intelligence in Breast Cancer Identification
  22. 9 Interpretability of Self-Supervised Learning for Breast Cancer Image Analysis
  23. 10 Predictive Analytics in Hospital Readmission for Diabetes Risk Patients
  24. 11 Continuous Blood Glucose Monitoring Using Explainable AI Techniques
  25. 12 Decision Support System for Facial Emotion-Based Progression Detection of Parkinson’s Patients
  26. 13 Interpretable Machine Learning in Athletics for Injury Risk Prediction
  27. 14 Federated Learning and Explainable AI in Healthcare
  28. Glossary
  29. Index