SynMatch：重新思考稀疏标注医学图像分割中的一致性
SynMatch: Rethinking Consistency in Medical Image Segmentation with Sparse Annotations
AI总结：
针对医学图像分割中伪标签与未标注图像不一致的问题，提出SynMatch框架，通过分割模型提取特征合成图像来匹配伪标签，在半监督、弱监督和极少监督设置下均取得优异性能。
摘要：
标签稀缺仍然是基于深度学习的医学图像分割面临的主要挑战。最近的研究使用强-弱伪监督来利用未标记数据。然而，性能常常受到伪标签与其对应的未标记图像之间不一致性的阻碍。在这项工作中，我们提出了SynMatch，这是一个新颖的框架，它通过合成图像来匹配伪标签，从而避开了改进伪标签的需求。具体而言，SynMatch使用从同一分割模型中提取的纹理和形状特征来合成图像，该模型为未标记图像生成相应的伪标签。这种设计使得生成高度一致的合成图像-伪标签对成为可能，而无需任何用于图像合成的训练参数。我们在半监督学习（SSL）、弱监督学习（WSL）和几乎无监督学习（BSL）设置下，对SynMatch在标注越来越有限的多种医学图像分割任务中进行了广泛评估。结果表明，SynMatch取得了卓越的性能，尤其是在最具挑战性的BSL设置中。例如，在具有5%和10%涂鸦标注的息肉分割任务上，它分别比最近基于强-弱伪监督的方法高出29.71%和10.05%。代码将在https://github.com/Senyh/SynMatch发布。
英文摘要：
Label scarcity remains a major challenge in deep learning-based medical image segmentation. Recent studies use strong-weak pseudo supervision to leverage unlabeled data. However, performance is often hindered by inconsistencies between pseudo labels and their corresponding unlabeled images. In this work, we propose \textbf{SynMatch}, a novel framework that sidesteps the need for improving pseudo labels by synthesizing images to match them instead. Specifically, SynMatch synthesizes images using texture and shape features extracted from the same segmentation model that generates the corresponding pseudo labels for unlabeled images. This design enables the generation of highly consistent synthesized-image-pseudo-label pairs without requiring any training parameters for image synthesis. We extensively evaluate SynMatch across diverse medical image segmentation tasks under semi-supervised learning (SSL), weakly-supervised learning (WSL), and barely-supervised learning (BSL) settings with increasingly limited annotations. The results demonstrate that SynMatch achieves superior performance, especially in the most challenging BSL setting. For example, it outperforms the recent strong-weak pseudo supervision-based method by 29.71\% and 10.05\% on the polyp segmentation task with 5\% and 10\% scribble annotations, respectively. The code will be released at https://github.com/Senyh/SynMatch.
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。
