S²ME：用于涂鸦监督息肉分割的空间-光谱互教与集成学习
S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation
AI总结：
针对涂鸦监督息肉分割中标签稀缺及模型泛化、鲁棒性不足的问题，提出S²ME框架，通过空间-光谱双分支互教和熵引导伪标签融合提升性能。
摘要：
多年来，全监督息肉分割在推动结直肠癌早期诊断方面取得了重大成功。然而，由于密集标注息肉数据昂贵且稀缺，来自涂鸦等弱监督的标签高效解决方案在医学实践中虽极具意义和需求，却很少得到探索。此外，数据偏移和损坏等各种部署问题对模型的泛化能力和鲁棒性提出了进一步要求。为解决这些问题，我们设计了一个空间-光谱双分支互教与熵引导伪标签集成学习框架（S²ME）。具体而言，我们在弱监督医学图像分割中首次利用从空间域和光谱域提取特征的内在互补性，并通过协同优化促进跨空间一致性，从而改进双分支协同教学框架。此外，为生成可靠的混合伪标签以增强集成学习的有效性，我们提出了一种新颖的基于空间和光谱分支熵引导的自适应像素级融合技术。我们的策略有效缓解了伪标签中存在的不确定性和噪声的有害影响，并在效能上超越了以往的替代方案。最后，我们构建了一个整体优化目标，以从涂鸦和伪标签的混合监督中学习。在四个公共数据集上的大量实验和评估证明了我们的方法在分布内准确性、分布外泛化和鲁棒性方面的优越性，凸显了其良好的临床意义。我们的代码可在 https://github.com/lofrienger/S2ME 获取。
英文摘要：
Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles are rarely explored yet primarily meaningful and demanding in medical practice due to the expensiveness and scarcity of densely-annotated polyp data. Besides, various deployment issues, including data shifts and corruption, put forward further requests for model generalization and robustness. To address these concerns, we design a framework of Spatial-Spectral Dual-branch Mutual Teaching and Entropy-guided Pseudo Label Ensemble Learning (S$^2$ME). Concretely, for the first time in weakly-supervised medical image segmentation, we promote the dual-branch co-teaching framework by leveraging the intrinsic complementarity of features extracted from the spatial and spectral domains and encouraging cross-space consistency through collaborative optimization. Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixel-wise fusion technique based on the entropy guidance from the spatial and spectral branches. Our strategy efficiently mitigates the deleterious effects of uncertainty and noise present in pseudo labels and surpasses previous alternatives in terms of efficacy. Ultimately, we formulate a holistic optimization objective to learn from the hybrid supervision of scribbles and pseudo labels. Extensive experiments and evaluation on four public datasets demonstrate the superiority of our method regarding in-distribution accuracy, out-of-distribution generalization, and robustness, highlighting its promising clinical significance. Our code is available at https://github.com/lofrienger/S2ME.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
