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Framework demonstrates tissue tracking performance with synthetic data, indicating potential for real-world application.
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.
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Inglezou et al. (2026) studied this question.
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