Inside Cover: Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman‐microscopy‐based cytopathology (J. Biophotonics 10/2018). Issue 10 (8th October 2018) - Krauß, Sascha D.; Roy, Raphael; Yosef, Hesham K.; Lechtonen, Tatjana; El‐Mashtoly, Samir F.; Gerwert, Klaus; Mosig, Axel
Abstract : 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 ) Abstract : 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 )