Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Neural Networks for the Correction of Mie Scattering in
  Fourier-Transformed Infrared Spectra of Biological Samples   Infrared spectra obtained from cell or tissue specimen have commonly been
observed to involve a significant degree of (resonant) Mie scattering, which
often overshadows biochemically relevant spectral information by a non-linear,
non-additive spectral component in Fourier transformed infrared (FTIR)
spectroscopic measurements. Correspondingly, many successful machine learning
approaches for FTIR spectra have relied on preprocessing procedures that
computationally remove the scattering components from an infrared spectrum. We
propose an approach to approximate this complex preprocessing function using
deep neural networks. As we demonstrate, the resulting model is not just
several orders of magnitudes faster, which is important for real-time clinical
applications, but also generalizes strongly across different tissue types.
Furthermore, our proposed method overcomes the trade-off between computation
time and the corrected spectrum being biased towards an artificial reference
spectrum.

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The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: econ
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: ao-sci