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

Journal of Biophotonics
Sascha D KraußAxel Mosig

Abstract

Hierarchical variants of so-called deep convolutional neural networks (DCNNs) have facilitated breakthrough results for numerous pattern recognition tasks in recent years. We assess the potential of these novel whole-image classifiers for Raman-microscopy-based cytopathology. Conceptually, DCNNs facilitate a flexible combination of spectral and spatial information for classifying cellular images as healthy or cancer-affected cells. As we demonstrate, this conceptual advantage translates into practice, where DCNNs exceed the accuracy of both conventional classifiers based on pixel spectra as well as classifiers based on morphological features extracted from Raman microscopic images. Remarkably, accuracies exceeding those of all previously proposed classifiers are obtained while using only a small fraction of the spectral information provided by the dataset. Overall, our results indicate a high potential for DCNNs in medical applications of not just Raman, but also infrared microscopy.

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Citations

Jul 21, 2020·Wiley Interdisciplinary Reviews. Systems Biology and Medicine·Anthony A Fung, Lingyan Shi
Nov 2, 2019·Nature Communications·Chi-Sing HoJennifer Dionne
Mar 3, 2020·Optics Express·Mingguang ShanYabin Zhang
Apr 17, 2021·Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy·Yuan WangXiangyue Chen
Aug 4, 2021·Applied Spectroscopy·Tatu RojalinRandy P Carney
Sep 5, 2019·Analytical Chemistry·Karim AljakouchSamir F El-Mashtoly

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