Medical imaging is a key component of modern healthcare, facilitating a wide array of diagnostic and therapeutic applications. Data-driven computer vision and AI solutions for medical imaging thereby represent a great potential to make a real-life impact by improving patient care.

However, safety requirements associated with healthcare pose major challenges for this research field, especially regarding data curation. Collection and annotation of medical data is often resource-intensive due to the need for medical expertise. At the same time, data quality is of the highest importance to ensure safe and fair usage in clinical settings. As a result, efficient data curation and validation as well as learning from small data are important areas of research. Synthetic data generation and augmentation are further promising directions, which themselves, however, pose challenges regarding quality, bias, and utility.

In addressing these demands, data engineering emerges as a crucial driver in advancing medical imaging research into deployment. Nevertheless, it is challenging to fulfill all the needs of task-specific applications via traditional methods. To bridge the gap, this workshop aims to encourage the discussion on topics related to push forward the frontier of data curation and augmentation for medical applications to tackle the challenges of limited or imperfect data in the real-world medical application.

We are excited to provide an interactive workshop platform to host topics related to medical imaging and medical computer vision, which include but are not limited to: