Tissue tracking under long-horizon occlusions with contrastive learning

Overall, the proposed framework provides a practical, self-supervised solution for long-horizon tissue tracking in minimally invasive surgery, demonstrating promising performance despite current quantitative evaluation being limited to synthetic data due to the lack of suitable real-world benchmarks. The code is available at https://github.com/Essex-AI-Innovation-Centre/cl-ve-tracking.