Explainable Artificial Intelligence for Biomedical Applications
eBook - ePub

Explainable Artificial Intelligence for Biomedical Applications

  1. 380 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Explainable Artificial Intelligence for Biomedical Applications

About this book

Since its first appearance, artificial intelligence has been ensuring revolutionary outcomes in the context of real-world problems. At this point, it has strong relations with biomedical and today's intelligent systems compete with human capabilities in medical tasks. However, advanced use of artificial intelligence causes intelligent systems to be black-box. That situation is not good for building trustworthy intelligent systems in medical applications. For a remarkable amount of time, researchers have tried to solve the black-box issue by using modular additions, which have led to the rise of the term: interpretable artificial intelligence. As the literature matured (as a result of, in particular, deep learning), that term transformed into explainable artificial intelligence (XAI).

This book provides an essential edited work regarding the latest advancements in explainable artificial intelligence (XAI) for biomedical applications. It includes not only introductive perspectives but also applied touches and discussions regarding critical problems as well as future insights.

Topics discussed in the book include:

  • XAI for the applications with medical images
  • XAI use cases for alternative medical data/task
  • Different XAI methods for biomedical applications
  • Reviews for the XAI research for critical biomedical problems.

Explainable Artificial Intelligence for Biomedical Applications is ideal for academicians, researchers, students, engineers, and experts from the fields of computer science, biomedical, medical, and health sciences. It also welcomes all readers of different fields to be informed about use cases of XAI in black-box artificial intelligence. In this sense, the book can be used for both teaching and reference source purposes.

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Yes, you can access Explainable Artificial Intelligence for Biomedical Applications by Utku Kose,Deepak Gupta,Xi Chen in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. Foreword
  9. Acknowledgements
  10. List of Contributors
  11. List of Figures
  12. List of Tables
  13. List of Abbreviations
  14. 1 Gastric Cancer Detection using Hybridbased Network and SHAP Analysis
  15. 2 LIME Approach in Diagnosing Diseases – A Study on Explainable AI
  16. 3 Explainable Artificial Intelligence (XAI) in the Veterinary and Animal Sciences Field
  17. 4 Interpretable Analysis of the Potential Impact of Various Versions of Corona Virus: A Case Study
  18. 5 XAI in Biomedical Applications
  19. 6 What Makes Survival of Heart Failure Patients? Prediction by the Iterative Learning Approach and Detailed Factor Analysis with the SHAP Algorithm
  20. 7 Class Activation Mapping and Deep Learning for Explainable Biomedical Applications
  21. 8 Pragmatic Study of IoT in Healthcare Security with an Explainable AI Perspective
  22. 9 Chest Disease Identification from X-rays using Deep Learning
  23. 10 Explainable Artificial Intelligence Applications in Dentistry: A Theoretical Research
  24. 11 Application of Explainable Artificial Intelligence in Drug Discovery and Drug Design
  25. 12 Automatic Segmentation of Spinal Cord Gray Matter from MR Images using a U-Net Architecture
  26. 13 XAI for Drug Discovery
  27. 14 Explainable Intelligence Enabled Smart Healthcare for Rural Communities
  28. 15 Explainable Artificial Intelligence in Drug Discovery for Biomedical Applications
  29. 16 XAI in the Hybrid Classification of Brain MRI Tumor Images
  30. 17 Comparative Analysis of Breast Cancer Diagnosis Driven by the Smart IoT-based Approach
  31. Index
  32. About the Editors