**NOTE** The submission deadline has been moved from June 25 to July 2.

Training machine learning systems for image recognition, object detection and image segmentation often requires a huge amount of expert annotated data to reach a high level of accuracy. Having a large number of labeled images helps to increase the performance of machine learning models by generalizing better and thereby reducing overfitting. Most popular benchmark datasets for general image recognition tasks have a few thousand to millions of images.

Obtaining such huge amounts of labeled data is very challenging in the medical imaging domain, however, due to costly annotation by domain experts and lack of high-quality anonymized data out of privacy concerns. Furthermore, there are unique challenges to collecting annotated medical datasets. For instance, examples of rare pathological conditions, although hard to obtain, are extremely important for accurate representation of the data distribution; there are often variations among experts who provide labels, especially for conditions that human experts are most confused about and need help the most.

The goal of this workshop is to bring together and create a discussion forum for researchers in the MICCAI community who are interested in the rigorous study of medical data as it relates to machine learning systems, who are developing and promoting novel techniques in data augmentation, labeing, and learning from small or imperfect data, who would like to contribute benchmark datasets, open challenges and tasks that enable fair comparisons among existing and new techniques, and who are applying such techniques to improving the performance of medical image computing systems. The workshop will have invited speakers who will speak about popular and emerging approaches for data augmentation or imputation, and learning from small or noisy medical data. The workshop welcomes submissions that present new ideas, new results, new datasets, as well as discussion and evaluation of existing approaches. The topics of interest include but are not limited to:

- Training and evaluation with noisy or uncertain labels
- Data annotation tools and practices
- Synthetic data for medical image analysis
- One-shot/few-shot learning
- Active learning
- Semi-, weakly-, self-supervised learning
- Deep learning for small, noisy and imperfect data
- Domain adaptation/generalization
- Erroneous label detection
- Data augmentation
- Data imputation
- Principles and/or case studies of annotated datasets and benchmarks
- Anonymization, PHI detection

Submissions to our workshop will be managed using the same platform as the main MICCAI conference, using the Microsoft CMT. Tentative conference submission website is at: https://cmt3.research.microsoft.com/DALI2021

Papers submitted to this workshop are limited to 8 pages including references. Papers should be formatted in Lecture Notes in Computer Science style. Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Supplemental material submission is optional, which may include:

- Videos of results that cannot be included in the main paper
- Anonymized related submissions to other conferences and journals
- Appendices or technical reports containing extended proofs and mathematical derivations that are not essential for the understanding of the paper

Contents of the supplemental material should be referred to appropriately in the paper and that reviewers are not obliged to look at it.