Rohr Group, Heidelberg University, Service center: Heidelberg Center for Human Bioinformatics – HD-HuB
Automated analysis of high-throughput and high-content microscopy image data is important to elucidate biological processes. However, analyzing such data poses a number of challenges. Recently, deep learning methods within the field of artificial intelligence emerged which yield superior results compared to classical image analysis methods.
The Biomedical Computer Vision Group at Heidelberg University is developing deep learning methods for computer-based analysis of cell microscopy image data (see de.NBI Services). The methods are based on deep neural networks that are trained from example data. Convolutional neural networks and recurrent neural networks are developed for different image analysis tasks such as cell segmentation and classification of microscopy images. The aim is to improve the automated quantification of cellular phenotypes at the single cell level as well as to efficiently process large scale microscopy data.
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