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

• 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.

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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

• 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.

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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

• 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.

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