About our workshop

Data-driven computer vision and AI solutions for medical imaging 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, learning from small data as well as data synthesis are important areas of research.

In addressing these demands, data engineering emerges as a crucial driver in advancing medical imaging research into deployment. 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:

• Data Selection and Curation
• Data Synthesis and Augmentation
• Learning with Limited and Imperfect Data. Incl.:
  • self-/semi-supervised learning
  • federated learning
  • active learning
  • domain adaptation
  • …
• Data Verification and Quality Assessment
• Effects of Data Domain Representation on Clinical Outputs
• From Data to Results: Learning from Success

This workshop will be held in conjunction with CVPR 2024 in Seattle and will include keynote presentations, panels and presentations of submitted papers. Follow us on X for latest news.

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