Segmentation of biomedical images with joint unsupervised learning

The convolutional neural networks with supervised training solve problems of semantic segmentation in a wide area of biomedical applications. However, this approach requires a significant size of the training dataset. Unsupervised learning can provide segmentation of some images without training dataset. Joint unsupervised learning is an approach that combines neural networks with another algorithm of data clusterization. In this preliminary study, we test the joint unsupervised learning approach with minor modifications for the problem of semantic segmentation at several datasets of biomedical images.