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Explainable AI in Healthcare de Mehul S Raval

Explainable AI in Healthcare: Analytics and AI for Healthcare

Editat de Mehul S Raval, Mohendra Roy, Rupal Kapdi, Tolga Kaya
en Limba Engleză Paperback – 13 apr 2025
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.
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Specificații

ISBN-13: 9781032367125
ISBN-10: 1032367121
Pagini: 304
Dimensiuni: 233 x 156 x 20 mm
Greutate: 0.49 kg
Editura: Taylor & Francis Ltd.
Seria Analytics and AI for Healthcare

Notă biografică

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

Cuprins

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.