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

In recent years, supervised machine learning approaches showed spectacular results in various image analysis problems [1]. Based on massive, annotated data sets, deep learning systems have come to the point where they are on par or even outperform humans in specific tasks [2] [3] [4]. However, fully annotated data sets are typically not available or even feasible to create in many domains. Manual reference annotations for pixel-level semantic segmentation in biomedical imaging are particularly costly as they can be too time-consuming and require considerable expert knowledge that might not readily be available. Here, semi-and self-supervised learning methods are promising approaches to build generalizable segmentation tools as they can leverage raw data and require only a few or no labels at all. These methods yield encouraging results in computer vision tasks on natural images.