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Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Alejandra M Fuentes1, Kirsty Milligan1, Mitchell Wiebe1

  • 1Department of Physics, The University of British Columbia Okanagan Campus, Kelowna, BC, Canada.

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|February 2, 2026
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Summary
This summary is machine-generated.

A new deep learning model automates Raman spectroscopy (RS) data pre-processing for cancer radiotherapy studies. This convolutional autoencoder (AE) efficiently removes artifacts from tumor cell and tissue spectra, improving analysis of radiation response.

Keywords:
CNNRaman spectroscopyconvolutional neural networksmachine learningradiation responsespectral pre-processing

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Area of Science:

  • Biomedical Optics
  • Computational Biology
  • Spectroscopy

Background:

  • Raman spectroscopy (RS) offers label-free molecular profiling for analyzing tumor response to radiotherapy.
  • Effective spectral pre-processing is crucial for accurate RS data analysis, involving baseline subtraction, smoothing, and artifact correction.
  • Current pre-processing methods can be time-consuming and require manual intervention.

Purpose of the Study:

  • To develop a single-step, automated spectral pre-processing method for Raman spectroscopy data from tumor cells and tissues.
  • To evaluate the performance of a convolutional autoencoder (AE) in removing spectral artifacts and improving data quality for radiotherapy studies.
  • To assess the AE's utility in identifying poor-quality spectra for semi-automated outlier detection.

Main Methods:

  • A convolutional autoencoder (AE) architecture was employed for automated spectral pre-processing.
  • Two AE models were trained: one for preclinical (cell line, xenograft) and one for clinical (prostate biopsy) Raman spectra.
  • The AE's performance was quantified using root mean squared error (RMSE) and percentage root mean squared difference (PRD) compared to a baseline algorithm.
  • A reconstruction AE was trained for semi-automated identification of poor-quality spectra.

Main Results:

  • The AE demonstrated rapid and effective removal of baseline, noise, and cosmic rays (CRs) from both preclinical and clinical spectra.
  • For preclinical data, AE achieved RMSE of 7.1 × 10-5 and PRD of 3.1%, removing 94.0% of CRs.
  • For clinical data, AE achieved RMSE of 8.1 × 10-5 and PRD of 3.7%, removing 90.2% of CRs.
  • The AE processed ~11,000 spectra in 2.4 seconds without a GPU.
  • A reconstruction AE achieved 96.4% agreement with manual outlier identification.

Conclusions:

  • The developed deep learning framework provides an efficient and automated solution for pre-processing tumor Raman spectra in radiation response studies.
  • The AE significantly enhances data quality, enabling consistent extraction of biochemical radiation response profiles.
  • This automated approach facilitates large-scale analysis of Raman spectroscopy data for clinical applications.