Trustworthy AI in Medical imaging

Marco Lorenzi is a tenured research scientist at the Inria Center of University Côte d’Azur (France), and junior chair holder at the Interdisciplinary Institute for Artificial Intelligence 3IA Côte d’Azur. He is also a visiting Senior Lecturer at the School of Biomedical Engineering & Imaging Sciences at King’s College London. His research focuses on developing statistical learning methods to model heterogeneous and secured data in biomedical applications. He is the founder and scientific responsible for the open-source federated learning platform Fed-BioMed. Dr Zuluaga is an assistant professor in the Data Science department at EURECOM. She holds a junior chair at the 3IA Institute Côte d’Azur and is a visiting Senior Lecturer within the School of Biomedical Engineering & Imaging Sciences at King’s College London. Her current research focuses on the development of machine learning techniques that can be safely deployed in high risk domains, such as healthcare, by addressing data complexity, low tolerance to errors and poor reproducibility.

Section 1: Robustness 1.1: Introduction 1.2: Uncertainty estimation and Calibration 1.3: Out-of-distribution Detection 1.4: Quality control Section 2: Validation, Transparency and Reproducibility 2.1: Introduction 2.2: Reproducibility in Medical Imaging Applications 2.3: Collaborative Validation and Performance Assessment in Medical Imaging Applications 2.4: Benchmarking and AI Challenges in the Medical Imaging Community Section 3: Bias and Fairness 3.1: Introduction 3.2: Addressing Fairness in Medical Imaging Applications 3.3: Model Bias in Medical Imaging Applications Section 4: Explainability, Interpretability and Causality 4.1: Introduction 4.2: Interpretability of AI in Medical Imaging and its links to fairness 4.3: Causality in Medical Imaging 4.4: Explainable AI in Medical Imaging Applications Section 5: Privacy-preserving ML 5.1: Introduction 5.2: Differential Privacy in Medical Imaging Applications 5.3: Encryption and Secured Computation in Medical Imaging Applications Section 6: Collaborative Learning 6.1: Introduction 6.2: Collaborative Learning in Medical Imaging 6.3: Real-World Deployment of Federated Learning in Lung Cancer Applications 6.4: Real-World Deployment of Federated Learning in xxx Section 7: Beyond the Technical Aspects 7.1: Introduction 7.2: AI Ethics in Medical Imaging (link to Section 1 and 2)