Preprint
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions--An EndoVis' 24 Challenge
arXiv preprint arXiv:2407.11906, 2024
Abstract
Surgical data science has seen rapid ad vancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes, DNNs have been proven susceptible to minor “corrup tions,” substantially impairing the model’s performance, introducing a major concern for the translation of cutting-edge technology, especially in high-stakes scenarios. We introduce the SegSTRONG-C challenge dedicated to better understanding model deterioration under unforeseen but plausible non-adversarial “corruption” and the capabilities of contemporary methods that seek to improve it. Built on a dataset generated through counterfactual robotic replay, SegSTRONG-C provides paired clean and “corrupted” samples, enabling reproducible evaluation of model robustness. Participants are challenged to train tool segmentation algorithms on “uncorrupted” data and evaluate them on “corrupted” test …
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Cite (BibTeX)
@article{ding2024segstrong,
author = {Ding, Hao and Zhang, Yuqian and Lu, Tuxun and Liang, Ruixing and Shu, Hongchao and Seenivasan, Lalithkumar and Long, Yonghao and Dou, Qi and Gao, Cong and Leng, Yicheng and others},
journal = {arXiv preprint arXiv:2407.11906},
title = {SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions--An EndoVis' 24 Challenge},
year = {2024}
}