Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology-Reference-Cited by-同舟云学术

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Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology

Published:2018-07-05 Issue:10 Volume:11 Page:e201800022
ISSN:1864-063X
Container-title:Journal of Biophotonics
language:en
Short-container-title:J. Biophotonics

Author:

Krauß Sascha D.¹^ORCID,Roy Raphael¹,Yosef Hesham K.¹^ORCID,Lechtonen Tatjana¹^ORCID,El-Mashtoly Samir F.¹^ORCID,Gerwert Klaus¹^ORCID,Mosig Axel¹

Affiliation:

1. Department of Biophysics; Ruhr-University Bochum; Bochum Germany

Funder

European Regional Development Fund, European Union and North-Rhine Westphalia, Germany

Ministry of Innovation, Science and Research (MIWF) of North-Rhine Westphalia, Germany

Protein Research Unit Ruhr within Europe (PURE)

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/jbio.201800022/fullpdf

Reference61 articles.

1. Raman spectroscopy for screening and diagnosis of cervical cancer

2. Raman spectroscopy and cytopathology of oral exfoliated cells for oral cancer diagnosis

3. Near-infrared Raman spectroscopy for optical diagnosis of lung cancer

4. Spectral histopathology of colon cancer tissue sections by Raman imaging with 532 nm excitation provides label free annotation of lymphocytes, erythrocytes and proliferating nuclei of cancer cells

5. Virtual staining of colon cancer tissue by label-free Raman micro-spectroscopy

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