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

• URL: http://arxiv.org/abs/2011.08076v2
• Date: Mon, 23 Nov 2020 13:03:10 GMT
• Title: A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
• Authors: Nastassya Horlava, Alisa Mironenko, Sebastian Niehaus, Sebastian Wagner, Ingo Roeder, Nico Scherf
• Abstract summary: Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. These networks are usually trained in a supervised manner, requiring large amounts of labelled training data. In this work, we validate alternative ways to train CNNs with fewer labels for biomedical image segmentation using.
• Score: 0.13701366534590495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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