Managing Medical Imaging Datasets: From Curation to Evaluation
High-quality data is the backbone of impactful medical AI. This workshop explores the full lifecycle of medical imaging datasets—from smart curation strategies and synthetic data generation to robust evaluation techniques. You will gain exposure to cutting-edge research and hands-on tools that are reshaping how imaging data is managed in medical AI workflows.
In the first half, leading researchers and practitioners will share best practices in dataset quality control, synthetic data generation, and reproducible evaluation. In the second half, participants will take part in a hands-on tutorial using FiftyOne, a powerful open-source toolkit, to explore and curate datasets across modalities such as X-ray, MRI, CT, and ultrasound.
Whether you are a researcher looking to improve dataset integrity, a clinician collaborating on AI development, or an ML engineer scaling healthcare models, this session offers practical insights and tools to level up your work with medical imaging data.
Target Audience:
- Medical imaging researchers
- Clinical collaborators in AI projects
- ML engineers and data scientists in healthcare
- Professionals involved in dataset annotation and evaluation
Tentative Program:
- Date: July 11, 08:30 AM to 12:30 PM
- Location: Taipei, Taiwan
- Capacity: In-person (room for up to 80 people)
- Time: Set by the workshop organizers
- Focused Topics
- First 2 hours: I4H intro hands-on training lab and Open Models
- Second 2 hours: PhysioMotion + MONAI — either run another 90-minute hands-on lab or give presentations and offer free training credits for self-paced learning at home
Workshop Venue: Chientan Youth Activity Center
