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In recent years, Convolutional Neural Networks (CNNs) have become thestate-of-the-art method for biomedical image analysis. However, these networksare usually trained in a supervised manner, requiring large amounts of labelledtraining data. These labelled data sets are often difficult to acquire in thebiomedical domain. In this work, we validate alternative ways to train CNNswith fewer labels for biomedical image segmentation using. We adapt two semi-and self-supervised image classification methods and analyse their performancefor semantic segmentation of biomedical microscopy images.

A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data