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MURDA: Multisource Unsupervised Raman Spectroscopy Domain Adaptation Model with Reconstructed Target Domains for

Yang Liu1, Chen Chen1,2, Enguang Zuo3

  • 1College of Software, Xinjiang University, Urumqi 830046, China.

Analytical Chemistry
|September 20, 2024
PubMed
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Multisource Unsupervised Raman Spectroscopy Domain Adaptation (MURDA) improves autoimmune disease diagnosis by transferring knowledge from multiple spectral data sources. This AI-driven method enhances accuracy without extensive labeled data, aiding medical diagnostics.

Area of Science:

  • Biomedical Engineering
  • Medical Informatics
  • Spectroscopy

Background:

  • Artificial intelligence (AI) and Raman spectroscopy show promise for disease diagnosis but require substantial annotated spectral data.
  • Labeling medical data is time-consuming and resource-intensive, hindering the development of accurate diagnostic models.
  • Existing methods often rely on single-source knowledge transfer, limiting generalization capabilities.

Purpose of the Study:

  • To develop a novel method, Multisource Unsupervised Raman Spectroscopy Domain Adaptation (MURDA), to reduce reliance on labeled medical data for AI-driven disease diagnosis.
  • To enhance the diagnostic accuracy for autoimmune diseases by leveraging knowledge from multiple source domains.
  • To introduce a specialized feature extractor, Double-Branch Multiscale Convolutional Self-Attention (DMCS), optimized for spectral data.

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Main Methods:

  • Proposed the MURDA model for unsupervised domain adaptation, transferring knowledge from multiple source disease datasets to an unlabeled target dataset.
  • Developed the DMCS feature extractor for improved extraction of relevant features from spectral data.
  • Applied MURDA and DMCS to serum Raman spectroscopy datasets for autoimmune disease diagnosis and validated on the RRUFF dataset.

Main Results:

  • MURDA achieved superior diagnostic accuracy compared to traditional single-source and multisource models on three autoimmune disease datasets (73.6%, 83.4%, 82.9%).
  • Significant improvements were observed compared to models without domain adaptation (15.1%, 36%, 21.6% increases).
  • The method demonstrated effectiveness and generalization across different Raman spectroscopy scenarios, including the public RRUFF dataset.

Conclusions:

  • Raman spectroscopy combined with the MURDA model is effective for diagnosing autoimmune diseases, significantly outperforming existing methods.
  • The study highlights the advantage of multisource knowledge transfer for improving AI diagnostic model robustness and accuracy.
  • Investigated key spectral peaks, offering insights for future AI and Raman spectroscopy research in disease diagnostics.