论文标题
对生物医学显微镜数据的半监督语义分割的比较研究
A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
论文作者
论文摘要
近年来，卷积神经网络（CNN）已成为生物医学图像分析的最新方法。但是，这些网络通常以监督方式进行培训，需要大量标记的培训数据。这些标记的数据集通常很难在生物医学领域中获取。在这项工作中，我们验证了培训使用更少标签的CNN进行生物医学图像分割的方法。我们适应两种半监督图像分类方法，并分析其性能，以进行生物医学显微镜图像的语义分割。
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of labelled training data. These labelled data sets are often difficult to acquire in the biomedical domain. In this work, we validate alternative ways to train CNNs with fewer labels for biomedical image segmentation using. We adapt two semi- and self-supervised image classification methods and analyse their performance for semantic segmentation of biomedical microscopy images.
