Explainable AI in Healthcare (eBook, PDF)

Unboxing Machine Learning for Biomedicine
Redaktion: Raval, Mehul S; Kapdi, Rupal; Kaya, Tolga; Roy, Mohendra

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 328
• Erscheinungstermin: 17. Juli 2023
• Englisch
• ISBN-13: 9781000906394
• Artikelnr.: 68011767

Autorenporträt

Mehul S Raval, Associate Dean - Experiential Learning and Professor, School of Engineering and Applied Science, Ahmedabad University, Ahmedabad, India Mohendra Roy, Assistant Professor, Information and Communication Technology Department, School of Technology, Pandit Deendayal Energy University, Gandhinagar, India Tolga Kaya, , Professor and Director of Engineering Programs, Sacred Heart University, Fairfield, CT, USA Rupal Kapdi, Assistant Professor, Computer Science and Engineering Department, Institute of Technology, Nirma University, Ahmedabad, India

Inhaltsangabe

1. Human-AI Relationship in Healthcare. 2. Deep Learning in Medical Image
Analysis: Recent Models and Explainability. 3. An Overview of Functional
Near-Infrared Spectroscopy and Explainable Artificial Intelligence 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 Tumor Patients Using Machine
Learning and Radiomics Features. 8. Explainable Artificial Intelligence 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.