MIL3ID 2019 Preface

摘要

MIL3ID 2019 is the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data. The MIL3ID 2019 proceedings contain 16 high-quality papers of 8 pages each, which were selected through a rigorous peer-review process. We hope this workshop will create a forum for discussing best practices in medical image learning with label scarcity and data imperfection. This forum is urgently needed because the issues of label noises and data scarcity are highly practical but largely under investigated in the medical image analysis community. Traditional approaches for dealing with these challenges include transfer learning, active learning, denoising, and sparse representation. The majority of these algorithms were developed prior to the recent advances of deep learning and might not benefit from the power of deep networks. The revision and improvement of these techniques in the new light of deep learning are long overdue.