Xiaoxiao Li - Böcker

Federated Learning for Medical Imaging: Principles, Algorithms, and Applications gives a deep understanding of the technology of federated learning (FL), the architecture of a federated system, and the algorithms for FL. It shows how FL allows multiple medical institutes to collaboratively train and use a precise machine learning (ML) model without sharing private medical data via practical implantation guidance. The book includes real-world case studies and applications of FL, demonstrating how this technology can be used to solve complex problems in medical imaging. The book also provides an understanding of the challenges and limitations of FL for medical imaging, including issues related to data and device heterogeneity, privacy concerns, synchronization and communication, etc.This book is a complete resource for computer scientists and engineers, as well as clinicians and medical care policy makers, wanting to learn about the application of federated learning to medical imaging.

Presents the specific challenges in developing and deploying FL to medical imagingExplains the tools for developing or using FLPresents the state-of-the-art algorithms in the field with open source software on GithubGives insight into potential issues and solutions of building FL infrastructures for real-world applicationInforms researchers on the future research challenges of building real-world FL applications

This book constitutes the refereed proceedings of the 10th International Workshop on Clinical Image-Based Procedures, CLIP 2021, Second MICCAI Workshop on Distributed and Collaborative Learning, DCL 2021, First MICCAI Workshop, LL-COVID19, First Secure and Privacy-Preserving Machine Learning for Medical Imaging Workshop and Tutorial, PPML 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic.CLIP 2021 accepted 9 papers from the 13 submissions received. It focuses on holistic patient models for personalized healthcare with the goal to bring basic research methods closer to the clinical practice.For DCL 2021, 4 papers from 7 submissions were accepted for publication. They deal with machine learning applied to problems where data cannot be stored in centralized databases and information privacy is a priority. LL-COVID19 2021 accepted 2 papers out of 3 submissions dealing with the use of AI models in clinical practice. And for PPML 2021, 2 papers were accepted from a total of 6 submissions, exploring the use of privacy techniques in the medical imaging community.

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.

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 LearningFirst 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 constitutes the proceedings from the workshops held at the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, which took place in Marrakesh, Morocco in October 2024.The 23 papers presented in this volume were carefully reviewed and selected from the total submissions from the following workshops: -Ninth International Skin Imaging Collaboration Workshop (ISIC 2024)Seventh International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2024)Embodied AI and Robotics for HealTHcare Workshop (EARTH 2024)Fifth MICCAI Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2024)

This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows:FL@FM-WWW 2024, FL@FM-ICME 2024, FL@FM-IJCAI 2024 and FL@FM-NeurIPS 2024.