Deep Learning of Cell Classification using Microscope Images of Intracellular Microtubule Networks
| Autor: | A. V. Burakov, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko, Aleksei Shpilman, Elena S. Nadezhdina |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Cell Computer Science - Computer Vision and Pattern Recognition Task (project management) Machine Learning (cs.LG) 03 medical and health sciences Kernel (linear algebra) Microtubule medicine FOS: Electrical engineering electronic engineering information engineering Relevance (information retrieval) Artificial neural network Contextual image classification business.industry Deep learning Image and Video Processing (eess.IV) Pattern recognition Electrical Engineering and Systems Science - Image and Video Processing 030104 developmental biology medicine.anatomical_structure Artificial intelligence business Intracellular |
| Zdroj: | ICMLA |
| DOI: | 10.48550/arxiv.2012.12125 |
| Popis: | Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistance. Therefore, the classification of MT images is of great relevance for cell diagnostics. Human experts find it particularly difficult to recognize the levels of chemical compound exposure of a cell. Improving the accuracy with automated techniques would have a significant impact on cell therapy. In this paper we present the application of Deep Learning to MT image classification and evaluate it on a large MT image dataset of animal cells with three degrees of exposure to a chemical agent. The results demonstrate that the learned deep network performs on par or better at the corresponding cell classification task than human experts. Specifically, we show that the task of recognizing different levels of chemical agent exposure can be handled significantly better by the neural network than by human experts. |
| Databáze: | OpenAIRE |
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