论文标题
肺超声图像中B线检测的半监督学习方法
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images
论文作者
论文摘要
研究证明，肺超声图像中的B线数量与血管外肺水的量有很强的统计联系，这对于血液透析治疗很重要。手动检查B线需要专家，并且耗时，而建模自动化方法目前由于缺乏地面真理而成为问题。因此，在本文中，我们提出了一种基于对比度学习的B线检测任务的新型半监督学习方法。通过对未标记的肺超声图像进行多层次的无监督学习，学习了手工艺品的特征。在下游任务中，我们使用基于EIOU的损失函数在少数标记的图像上引入了微调过程。除了减少数据标记工作量外，提出的方法还显示出与基于模型的算法相比，召回91.43％，精度为84.21％，F1得分为91.43％。
Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment. Manual inspection of B-lines requires experts and is time-consuming, whilst modelling automation methods is currently problematic because of a lack of ground truth. Therefore, in this paper, we propose a novel semi-supervised learning method for the B-line detection task based on contrastive learning. Through multi-level unsupervised learning on unlabelled lung ultrasound images, the features of the artefacts are learnt. In the downstream task, we introduce a fine-tuning process on a small number of labelled images using the EIoU-based loss function. Apart from reducing the data labelling workload, the proposed method shows a superior performance to model-based algorithm with the recall of 91.43%, the accuracy of 84.21% and the F1 score of 91.43%.
