Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning

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Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning is written by Cristina Oyarzun Laura and published by Springer Nature. It's available with International Standard Book Number or ISBN identification 3030908747 (ISBN 10) and 9783030908744 (ISBN 13).

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.