Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains.

MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection. 

• artificial intelligence
• ct image
• image analysis
• image reconstruction
• image segmentation
• imaging systems
• learning algorithms
• machine learning
• medical images
• medical imaging
• neural networks
• segmentation methods
• semi-supervised learning
• supervised learning

• Shanghai Jiaotong University, Shanghai, China

  Qian Wang

• NVIDIA GmbH, Munich, Germany

  Fausto Milletari, Nicola Rieke

• University of Houston, Houston, USA

  Hien V. Nguyen, Badri Roysam

• Technical University Munich, Munich, Germany

  Shadi Albarqouni

• King's College London, London, UK

  M. Jorge Cardoso

• NVIDIA, Santa Clara, USA

  Ziyue Xu

• Imperial College London, London, UK

  Konstantinos Kamnitsas

• Johns Hopkins University, Baltimore, USA

  Vishal Patel

• UT Southwestern Medical Center, Dallas, USA

  Steve Jiang

• Chinese Academy of Sciences, Beijing, China

  Kevin Zhou

• University of Arkansas, Fayetteville, USA

  Khoa Luu, Ngan Le

Policies and ethics