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On the expansion of training datasets to improve deep learning-based cell organelle recognition in fluorescence microscopy images of virus-infected cells

Authors: Busch, Nils; Rausch, Andreas; Schanze, Thomas;

On the expansion of training datasets to improve deep learning-based cell organelle recognition in fluorescence microscopy images of virus-infected cells

Abstract

The Institute for Virology, Philipps-University, Marburg, developed a method to create image sequences of Marburg virus-infected live-cells. This work focuses on the expansion of image datasets by various transformation techniques to improve the training of a neural network for pattern recognition, i.e., the detection and classification of cell structures by means of pure green fluorescent imaging of subviral particles The results show a high potential for automated segmentation of cell organelles.

Keywords

subviral-particles, object detection, deep-learning, AUTOMED2021, data augmentation

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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