论文标题
FixMatchseg：修复半监督语义分段的FixMatch
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation
论文作者
论文摘要
在过去的几年中，有监督的语义医学图像细分方法越来越流行。在资源约束的设置中，获得大量带注释的图像非常困难，因为它主要需要专家，既昂贵又耗时。Semi-Supuredsubistion的分割可以是一个有吸引力的解决方案，可以在很少的标记图像中使用，与大型图像一起使用，大型的图像是一件大型的，这些片段可以使用。尽管在过去的几年中，针对分类问题的监督和半监督方法之间的差距已大大减少，但分割方法仍然存在较大的差距。在这项工作中，我们将最先进的半监督分类方法FIXMATCH修复到语义分割任务中，并引入FixMatchSeg。 FIXMATCHSEG在不同的不同解剖结构和不同方式的四个不同的公开数据集中进行了评估：心脏超声，胸部X射线，视网膜眼镜图像和皮肤图像。当标签很少时，我们表明FixMatchSeg与强有力的监督基线相同。
Supervised deep learning methods for semantic medical image segmentation are getting increasingly popular in the past few years.However, in resource constrained settings, getting large number of annotated images is very difficult as it mostly requires experts, is expensive and time-consuming.Semi-supervised segmentation can be an attractive solution where a very few labeled images are used along with a large number of unlabeled ones. While the gap between supervised and semi-supervised methods have been dramatically reduced for classification problems in the past couple of years, there still remains a larger gap in segmentation methods. In this work, we adapt a state-of-the-art semi-supervised classification method FixMatch to semantic segmentation task, introducing FixMatchSeg. FixMatchSeg is evaluated in four different publicly available datasets of different anatomy and different modality: cardiac ultrasound, chest X-ray, retinal fundus image, and skin images. When there are few labels, we show that FixMatchSeg performs on par with strong supervised baselines.
