Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution
buir.contributor.author | Çetin, A. Enis | |
buir.contributor.orcid | Çetin, A. Enis|0000-0002-3449-1958 | |
dc.citation.epage | 6 | en_US |
dc.citation.spage | 1 | en_US |
dc.citation.volumeNumber | 10140 | en_US |
dc.contributor.author | Aichinger, W. | en_US |
dc.contributor.author | Krappe, S. | en_US |
dc.contributor.author | Çetin, A. Enis | en_US |
dc.contributor.author | Çetin-Atalay, R. | en_US |
dc.contributor.author | Üner, A. | en_US |
dc.contributor.author | Benz, M. | en_US |
dc.contributor.author | Wittenberg, T. | en_US |
dc.contributor.author | Stamminger, M. | en_US |
dc.contributor.author | Münzenmayer, C. | en_US |
dc.coverage.spatial | Orlando, Florida, United States | en_US |
dc.date.accessioned | 2018-04-12T11:44:00Z | en_US |
dc.date.available | 2018-04-12T11:44:00Z | en_US |
dc.date.issued | 2017 | en_US |
dc.department | Department of Mechanical Engineering | en_US |
dc.description | Date of Conference: 11-16 February 2017 | en_US |
dc.description | Conference Name: SPIE Medical Imaging, 2017 | en_US |
dc.description.abstract | 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 and Eosin (HE) staining as an inexpensive tool for routine histopathology based on their distinct morphological features. We present initial results of a new method based on color deconvolution (CD) and convolutional neural networks (CNN). This method performs favorably (accuracy 0.936) in comparison with a state-of-the-art method based on 1DSIFT and eigen-analysis feature sets evaluated on the same image database. We also show that accuracy of the CNN is improved by the CD pre-processing. | en_US |
dc.description.provenance | Made available in DSpace on 2018-04-12T11:44:00Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017 | en |
dc.identifier.doi | 10.1117/12.2254036 | en_US |
dc.identifier.issn | 1605-7422 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37562 | en_US |
dc.language.iso | English | en_US |
dc.publisher | SPIE | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1117/12.2254036 | en_US |
dc.source.title | Proceedings of SPIE Vol. 10140, Medical Imaging 2017: Digital Pathology | en_US |
dc.subject | Color deconvolution | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Digital pathology | en_US |
dc.subject | Histopathology | en_US |
dc.subject | Texture analysis | en_US |
dc.title | Automated cancer stem cell recognition in H and E stained tissue using convolutional neural networks and color deconvolution | en_US |
dc.type | Conference Paper | en_US |
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