Inside Cover: Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman‐microscopy‐based cytopathology (J. Biophotonics 10/2018)

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. (https://doi.org/10.1002/e201800022)

Towards an Interpretable Classifier for Characterization of Endoscopic Mayo Scores in Ulcerative Colitis Using Raman Spectroscopy

“…In this work, characterization of inflammation in UC patients based on four Mayo endoscopic subscores was achieved. For this purpose, Raman spectroscopic data and deep convolutional neural networks (DCNNs) were utilized. A predictive modeling was performed using a two-layered one-dimensional deep convolutional neural network (1D-CNN) such that the input to the 1D-CNN was a Raman spectrum and the output was a probability of the spectrum belonging to one of the four Mayo endoscopic subscores.…”