Shadi Albarqouni

M. Jorge Cardoso

Spyridon Bakas

Konstantinos Kamnitsas

Ziyue Xu

Nicola Rieke

Bennett Landman

Wenqi Li

Fausto Milletari

Holger Roth

Daguang Xu

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus 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; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.

Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings

Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

Georg Langs

Bjoern Menze

Henning Müller

Albert Montillo

Mark Jenkinson

Tal Arbel

B. Michael Kelm

Weidong Cai

Dimitris Metaxas

William M. Wells III

Shaoting Zhang

Albert C.S. Chung

Annemie Ribbens

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big data” algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis.

MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Daniel C. Alexander

Sebastien Ourselin

Carl-Fredrik Westin

This book constitutes the proceedings of the 24th International Conference on Information Processing in Medical Imaging, IPMI 2015, held at the Sabhal Mor Ostaig College on the Isle of Skye, Scotland, UK, in June/July 2015. The 22 full papers and 41 poster papers presented in this volume were carefully reviewed and selected from 195 submissions. They were organized in topical sections named: probabilistic graphical models; MRI reconstruction; clustering; statistical methods; longitudinal analysis; microstructure imaging; shape analysis; multi-atlas fusion; fast image registration; deformation models; and the poster session.

24th International Conference, IPMI 2015, Sabhal Mor Ostaig, Isle of Skye, UK, June 28 - July 3, 2015, Proceedings

Information Processing in Medical Imaging

M. Jorge Cardoso

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