论文标题
自我监督的学习是减少医学图像分析中标记数据的需求的一种手段
Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis
论文作者
论文摘要
医学图像处理中最大的问题之一是缺乏带注释的数据。标记医学图像通常需要训练有素的专家，并且可能是一个耗时的过程。在本文中，我们评估了一种通过使用自我监督的神经网络预处理来减少医学图像对象检测中标记数据的需求的方法。我们使用带有边界框标签的胸部X射线图像的数据集用于13种不同类别的异常。这些网络在没有标签的数据集中鉴定在一定比例的数据集上，然后在数据集的其余部分进行微调。我们表明，只有60 \％的标记数据，就可以以平均平均精度和准确性来实现与完全监督模型相似的性能。我们还表明，可以通过添加自我监督的预处理步骤来提高完全监督模型的最大性能，并且即使有少量未标记的数据进行预处理也可以观察到这种效果。
One of the largest problems in medical image processing is the lack of annotated data. Labeling medical images often requires highly trained experts and can be a time-consuming process. In this paper, we evaluate a method of reducing the need for labeled data in medical image object detection by using self-supervised neural network pretraining. We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. The networks are pretrained on a percentage of the dataset without labels and then fine-tuned on the rest of the dataset. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60\% of the labeled data. We also show that it is possible to increase the maximum performance of a fully-supervised model by adding a self-supervised pretraining step, and this effect can be observed with even a small amount of unlabeled data for pretraining.
