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
This title covers computer vision and machine learning (ML) advances that facilitate automation in diagnostic, therapeutic, and preventative healthcare. The book shows the development of algorithms and architectures for healthcare.
1. HumanAI 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 Parkinsons Patients.
13.
Interpretable Machine Learning in Athletics for Injury Risk Prediction.
14.
Federated Learning and Explainable AI in Healthcare.
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
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