Xiaoxiao Li – författare

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.

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.

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 papers of the workshops are presenting the topical sections: 

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: -

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. This LNAI volume focuses on the following topics:

Efficient Model Adaptation and Personalization, Data Heterogeneity and Incomplete Data, Integration of Specialized Neural Architectures, Frameworks and Tools for Federated Learning, Applications in Domain-Specific Contexts, Unsupervised and Lightweight Learning, and Causal Discovery and Black-Box Optimization.