This book constitutes the refereed proceedings of the Third MICCAI Workshop on Distributed, Collaborative, and Federated Learning, DeCaF 2022, and the Second MICCAI Workshop on Affordable AI and Healthcare, FAIR 2022, held in conjunction with MICCAI 2022, in Singapore in September 2022. FAIR 2022 was held as a hybrid event. DeCaF 2022 accepted 14 papers from the 18 submissions received. The workshop aims at creating a scientific discussion focusing on the comparison, evaluation, and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases or where information privacy is a priority. For FAIR 2022, 4 papers from 9 submissions were accepted for publication. The topics of the accepted submissions focus on deep ultrasound segmentation, portable OCT image quality enhancement, self-attention deep networks and knowledge distillation in low-regime setting.

This double volume set LNCS 14393-14394 constitutes the proceedings from the workshops held at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023 Workshops, which took place in Vancouver, BC, Canada, in October 2023. The 54 full papers together with 14 short papers presented in this volume were carefully reviewed and selected from 123 submissions from all workshops. The papers of the workshops are presenting the topical sections: Eighth International Skin Imaging Collaboration Workshop (ISIC 2023) First Clinically-Oriented and Responsible AI for Medical Data Analysis (Care-AI 2023) Workshop First International Workshop on Foundation Models for Medical Artificial General Intelligence (MedAGI 2023) Fourth Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2023) First MICCAI Workshop on Time-Series Data Analytics and Learning First MICCAI Workshop on Lesion Evaluation and Assessment with Follow-Up (LEAF) AI For Treatment Response Assessment and predicTion Workshop (AI4Treat 2023) Fourth International Workshop on Multiscale Multimodal Medical Imaging (MMMI 2023) Second International Workshop on Resource-Effcient Medical Multimodal Medical Imaging Image Analysis (REMIA 2023)

This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data