Isabel Funke

About

Isabel Funke is a rising researcher at the intersection of computer vision and computer-assisted surgery, whose work is shaping how machines understand and analyze surgical video. Her primary research areas include surgical gesture recognition, surgical phase recognition, and the development of robust evaluation metrics for context-aware surgical systems. Funke’s most cited paper, "Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video" (2019, 13 citations), pioneered the application of 3D CNNs to capture both spatial and temporal dynamics of surgical motions, enabling more accurate and automated analysis of surgical workflows. Her 2023 work, "Metrics Matter in Surgical Phase Recognition" (8 citations), critically addresses the often-overlooked issue of evaluation standards in the field, arguing that meaningful comparison of phase recognition methods requires careful metric selection. This contribution highlights Funke’s commitment to methodological rigor and reproducibility. Though early in her career, her focused contributions are already informing the development of intelligent surgical assistants, and her emphasis on proper benchmarking is setting a new standard for the community.

Research Focus

Key Achievements

Top Papers

1. 1
   Using 3D Convolutional Neural Networks to Learn Spatiotemporal Features for Automatic Surgical Gesture Recognition in Video

   13 citations · 2019
2. 2
   Metrics Matter in Surgical Phase Recognition

   8 citations · 2023

Key Collaborators

Contact & Links