Automated cancer stem cell recognition in H&E stained tissue using convolutional neural networks and color deconvolution

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Date

2017-02-13

Author

Aichinger, Wolfgang
Krappe, Sebastian
ÇETİN, AHMET ENİS
Atalay, Rengül
ÜNER, AYŞEGÜL
Benz, Michaela
Wittenberg, Thomas
Stamminger, Marc
Muenzenmayer, Christian

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our work we aim at identifying CSC presence on ubiquitous Hematoxilyn & Eosin (H&E) staining as an inexpensive tool for routine histopathology based on their distinct morphological features.

Subject Keywords

Deep learning, Digital pathology, Histopathology, Convolutional neural network, Color deconvolution, Texture analysis

URI

https://hdl.handle.net/11511/63051

DOI

https://doi.org/10.1117/12.2254036

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Graduate School of Informatics, Conference / Seminar

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Citation Formats

W. Aichinger et al., “Automated cancer stem cell recognition in H&E stained tissue using convolutional neural networks and color deconvolution,” 2017, vol. 10140, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63051.