Surgical Data Science Summer School 2026
Overview
The Surgical Data Science (SDS) Summer School, hosted by IHU Strasbourg and the CAMMA research group, is one of the leading intensive programmes at the intersection of computer vision, machine learning, and clinical surgery. The 2026 edition brought together computer scientists, engineers, and clinicians from across Europe and beyond for a week of keynotes, hands-on labs, and collaborative projects.
I applied for in-person participation and was placed on the waiting list. Due to logistical constraints around Schengen visa timelines from the UK, I transitioned to the online track. The organising committee waived my registration fees in recognition of my status as a self-funded early-career researcher.
What I Attended
As an online participant I had full access to all keynote lectures across the five-day programme, covering a broad range of topics directly relevant to my research in medical AI:
- Surgical video analysis & workflow recognition - Nicolas Padoy (CAMMA, IHU Strasbourg & University of Strasbourg)
- Multimodal AI in Cardiovascular Medicine - Sandy Engelhardt (Heidelberg University Hospital)
- Surgical Data Science for Surgical Skills - Sofia Bano (University College London)
- Large-scale pre-training for surgical computer vision - Kun Yuan (CAMMA)
- Holistic OR Modelling - Nassir Navab (Technische Universität München)
- Evidence-based clinical translation: The IDEAL Framework - Peter McCulloch (Oxford University)
- Cut, code & co: a manifesto on surgical innovation - Michele Diana (University Hospital of Geneva)
Relevance to My Research
My independent research in computational histopathology, specifically domain generalisation of nuclei segmentation models sits at the same clinical AI frontier that surgical data science addresses: building models that are robust, interpretable, and trustworthy enough for real clinical deployment. Attending this school deepened my understanding of how the field approaches the gap between model performance in controlled settings and performance at the bedside, a challenge central to my own thesis work.
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