1st Edition

Explainable AI in Healthcare Unboxing Machine Learning for Biomedicine

Edited By Mehul S Raval, Mohendra Roy, Tolga Kaya, Rupal Kapdi Copyright 2024
    328 Pages 136 B/W Illustrations
    by Chapman & Hall

    328 Pages 136 B/W Illustrations
    by Chapman & Hall

    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


    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