1. DASH UMCG’s Data Science Center in Health
Peter van Ooijen, MSc, PhD, CPHIMS Data Science Center in Health (DASH) – Machine Learning Lab

2. .
DASH
UMCG’s Data Science Center in Health
Aim To stimulate awareness and advancements in data science and AI within the UMCG, locally and regionally and to become the center of expertise for data science in health in the Northern region. Actions Support healthcare professionals, researchers, educators, students and private partners with respect to machine learning, data science and AI.

3. .
DASH
UMCG’s Data Science Center in Health
Activities ‘Community building’, educational activities, DASH Projects, DASH Machine Learning Lab, (help to) acquire funding, networking, and linking up with regional/ (inter)national initiatives. DASH team Program Manager, MLL Coordinator, Liaison Officer/Staff advisor, Project & Product portfolio manager, secretarial support and two advisors.

4. DASH DASH Machine Learning Lab.
DASH
UMCG’s Data Science Center in Health
DASH Machine Learning Lab
Support the UMCG community with respect to the knowledge and utilization of data science/AI. Activities:
ML advice, knowledge and support
Connect broad ML community at the UMCG
Challenge participation with UMCG DASH team
Organize/execute pilot projects for UMCG researchers
MLL team
One associate professor, two postdocs and six PhD students,

5. Example 1 MR Mammo Tumor Detection
T1_pre
T1_post
DWI
Twist
Twist_early
Twist_later T1
Composite image
Exam 1
Exam 2
Diagram illustrates the generation of composite images
SLIDE COURTESY XEUPING JING, PhD

6. Example 1 - MR Mammo Tumor Detection
Three different cases and corresponding heatmaps generated by gradCAM.
SLIDE COURTESY XEUPING JING, PhD

7. Example 2 - Mandible Segmentation CT
Machine Learning/Deep Learning
Qiu et al. Phys Med Biol 2019;64(17):175020
SLIDE COURTESY BINGJIANG QIU, PHD

8. Example 3 - Synthetic imaging (GAN)
MR Synth CT Real CT Difference
SLIDE COURTESY BINGJIANG QIU, PHD

9. DASH Challenges Availability of data.
DASH
UMCG’s Data Science Center in Health
Challenges
Availability of data
Obtaining correct annotation/labeling
Integration of AI/Data Science into the clinical workflow
Quality control of AI tools
Acceptance of new tools by the user

10. .
DASH
UMCG’s Data Science Center in Health
Looking for…
Collaboration with health professionals and researchers
Collaborations with private partner / businesses
Co-creation
Incubator
Implementation partner
AI-related innovations