1st Edition
Explainable AI in Healthcare Unboxing Machine Learning for Biomedicine
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
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 Healthcare
Anca Bucur, Francesca Manni, Aleksandr Bukharev, Shiva Moorthy, Nancy Irisarri Mendez, and Anshul Jain
Biography
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
