Robust surgical tool segmentation with state-space vision models
This exploratory collaboration investigated state-space vision models for robust surgical tool segmentation under realistic visual corruptions.
Abstract
This exploratory collaboration investigated state-space vision models for robust surgical tool segmentation under realistic visual corruptions. In collaboration with Kyung Hee University, we participated in the MICCAI EndoVis 2024 SegSTRONG-C challenge, benchmarking robustness to non-adversarial corruptions such as smoke, bleeding, and illumination changes. The proposed Vision Mamba–UNet approach achieved competitive performance across corruption types and ranked 7th overall among international submissions.
Perspective
The project supported early exploration of state-space models in medical image analysis and international doctoral mentorship. A jointly submitted DAAD exchange proposal, although not funded, laid the groundwork for continued collaboration and future joint research activities.
