The 3rd MICCAI Workshop on Data Augmentation, Labeling, and Imperfections (DALI) is delighted to invite researchers worldwide to submit their work. DALI aims to create a collaborative environment for discussions on the challenges associated with the rapid expansion of data-intensive methods for supervised learning, particularly in the medical imaging domain.

We welcome researchers who are interested in the rigorous study of medical data in relation to machine learning systems, developing and promoting novel research directions, contributing benchmark datasets, open challenges, and tasks, and applying these techniques to improve the performance of medical image computing systems.

Our workshop will feature invited speakers discussing popular and emerging data augmentation and contemporary approaches for learning from small and noisy medical data. We encourage submissions that present new ideas, results, datasets, and discussions and evaluations of existing approaches. 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, multi-modal learning, active learning, domain adaptation, and much more.

Submissions should be managed using the same platform as the main MICCAI conference, using Microsoft CMT. The paper submission deadline is July 10, 2023, and the workshop day is October 12, 2023, in Vancouver, Canada. Manuscripts should be up to 8 pages (text, figures, and tables) plus up to 2 pages of references.

A selection of the best papers will be invited to submit revised and extended versions of their work to a Special Issue in Computerized Medical Imaging and Graphics (CMIG, IF:7.422). Two prestigious awards, the Best Paper Award and People’s Choice Award, each with $500, will also be presented at the workshop.

For more information, please visit our website at https://dali-miccai.github.io/.