Endoscopic Vision Challenge 2026

With the advent of artificial intelligence as key technology in modern medicine, surgical data science (SDS) promises to improve the quality and value of the particular domain of interventional healthcare through capturing, organization, analysis, and modeling of data, thus creating benefit for both patients and medical staff. Holistic SDS concepts span the topics of context-aware perception in and beyond the operating room, data interpretation and real-time assistance or decision support. At the same time, minimally invasive surgery using cameras to observe the internal anatomy has become the state-of-the-art approach to many surgical procedures. Contributing to the key aspect of perception, endoscopic vision thus constitutes a central component of SDS and computer-assisted interventions. From this arises the necessity for high-quality common datasets that allow the scientific community to perform comparative benchmarking and validation of endoscopic vision algorithms.

EndoVis (http://endovis.org/) organizes highprofile international challenges for the comparative validation of endoscopic vision algorithms that focus on different problems each year at MICCAI, comprising various computer vision tasks (classification, segmentation, detection, localization, etc) and subdisciplines ranging from laparoscopy to coloscopy and surgical training. It acts umbrella for several sub-challenges in this field, this year we propose 6 different sub-challenges within EndoVis:

• RARE26: Recognition of Anomalies in low-pREvalance cancer
• SurgVu26: Surgical Visual Understanding
• STIR26: Surgical Tissue Tracking Using the STIR Dataset
• TIGER-SQ-AI2026: AI-based surgical quality assessment for thoracic lymphadenectomy in minimally invasive esophagectomy
• CLiMB26: Colonoscopy Localization and Mapping Benchmark
• iMED26: Multi-Endoscope Dataset Novel View Synthesis Challenge