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Inside Cover: Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman‐microscopy‐based cytopathology (J. Biophotonics 10/2018)
Author(s) -
Krauß Sascha D.,
Roy Raphael,
Yosef Hesham K.,
Lechtonen Tatjana,
ElMashtoly Samir F.,
Gerwert Klaus,
Mosig Axel
Publication year - 2018
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201870163
Subject(s) - biophotonics , convolutional neural network , raman spectroscopy , cover (algebra) , microscopy , artificial intelligence , layer (electronics) , multispectral image , convolution (computer science) , computer science , pattern recognition (psychology) , optics , computer vision , physics , materials science , artificial neural network , nanotechnology , photonics , engineering , mechanical engineering
The image displays the construction of the first layers of a deep convolutional neural network for classifying Raman microscopic images of urotheleal cells as either cancerous or normal. The input layer is obtained by the intensity images of two selected wavenumbers, indicated as red and green, respectively. Deeper layers are constructed by convolution operations indicated in the yellow box. The final layer (not shown) will distinguish the two cell types. Further details can be found in the article by Sascha D. Krauß, Raphael Roy, Hesham K. Yosef, et al. ( e201800022 )