⚠️ 以下所有内容总结都来自于 大语言模型的能力，如有错误，仅供参考，谨慎使用
🔴 请注意：千万不要用于严肃的学术场景，只能用于论文阅读前的初筛！
💗 如果您觉得我们的项目对您有帮助 ChatPaperFree ，还请您给我们一些鼓励！⭐️ HuggingFace免费体验
2025-06-27 更新
C3S3: Complementary Competition and Contrastive Selection for Semi-Supervised Medical Image Segmentation
Authors:Jiaying He, Yitong Lin, Jiahe Chen, Honghui Xu, Jianwei Zheng
For the immanent challenge of insufficiently annotated samples in the medical field, semi-supervised medical image segmentation (SSMIS) offers a promising solution. Despite achieving impressive results in delineating primary target areas, most current methodologies struggle to precisely capture the subtle details of boundaries. This deficiency often leads to significant diagnostic inaccuracies. To tackle this issue, we introduce C3S3, a novel semi-supervised segmentation model that synergistically integrates complementary competition and contrastive selection. This design significantly sharpens boundary delineation and enhances overall precision. Specifically, we develop an Outcome-Driven Contrastive Learning module dedicated to refining boundary localization. Additionally, we incorporate a Dynamic Complementary Competition module that leverages two high-performing sub-networks to generate pseudo-labels, thereby further improving segmentation quality. The proposed C3S3 undergoes rigorous validation on two publicly accessible datasets, encompassing the practices of both MRI and CT scans. The results demonstrate that our method achieves superior performance compared to previous cutting-edge competitors. Especially, on the 95HD and ASD metrics, our approach achieves a notable improvement of at least 6%, highlighting the significant advancements. The code is available at https://github.com/Y-TARL/C3S3.
针对医学领域中标注样本不足这一迫在眉睫的挑战，半监督医学图像分割（SSMIS）提供了一个有前景的解决方案。尽管在勾勒主要目标区域方面取得了令人印象深刻的结果，但大多数当前的方法在精确捕捉边界的细微细节方面遇到了困难。这种缺陷往往会导致诊断上的重大失误。为了解决这个问题，我们引入了C3S3，这是一种新型的半监督分割模型，它协同整合了互补的竞争和对比选择。这种设计显著地提高了边界的清晰度，并提高了整体的精确性。具体来说，我们开发了一个结果驱动的对比学习模块，专门用于改进边界定位。此外，我们加入了一个动态互补竞争模块，该模块利用两个高性能子网络来生成伪标签，从而进一步提高分割质量。所提出的C3S3在两个公开数据集上进行了严格验证，涵盖了MRI和CT扫描的实践。结果表明，我们的方法相较于之前的顶尖竞争对手取得了优越的性能。特别是在95HD和ASD指标上，我们的方法取得了至少6%的显著改进，突显了重大进展。代码可通过https://github.com/Y-TARL/C3S3获取。
论文及项目相关链接
PDF Accepted to ICME 2025
Summary
一种名为C3S3的新型半监督分割模型被提出，用于解决医学图像分割中样本标注不足的问题。该模型通过融合对比学习和互补竞争机制，提高了边界勾勒的精准度，从而提高了诊断的准确性。C3S3在MRI和CT扫描的公开数据集上进行了验证，并相较于前沿方法取得了显著优势，特别是在95HD和ASD指标上改进了至少6%。
Key Takeaways
- C3S3是一种半监督分割模型，旨在解决医学图像分割中样本标注不足的问题。
- C3S3融合了对比学习和互补竞争机制，以提高边界勾勒的精准度。
- C3S3模型通过开发一个成果驱动对比学习模块，专门用于改进边界定位。
- C3S3模型还引入了一个动态互补竞争模块，利用两个高性能子网络生成伪标签，进一步提高了分割质量。
- C3S3在MRI和CT扫描的公开数据集上进行了验证，表现出卓越性能。
- 与前沿方法相比，C3S3在95HD和ASD指标上改进了至少6%，显示了显著的进步。
点此查看论文截图
