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Updated: Feb 13, 2026

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Performance of Naiive Spectral Geometric Models in Histopathology AI.

Alejandro Leyva1, M Khalid Khan Niazi2

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|February 12, 2026
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Summary

Purely spectral models in digital pathology did not outperform convolutional neural networks (CNNs) alone. However, spectral methods offer complementary interpretability and denoising utility, especially in data-limited scenarios.

Keywords:
AIDeep LearningMachine LearningSegmentationSpectral Geometry

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Area of Science:

  • Digital pathology
  • Computational imaging
  • Machine learning for medical imaging

Background:

  • Systematic evaluations of spectral models for digital pathology are lacking.
  • Convolutional neural networks (CNNs) are standard for image analysis tasks.

Purpose of the Study:

  • To systematically evaluate purely spectral models against CNN baselines across diverse digital pathology tasks.
  • To assess the utility of spectral models as complementary tools for interpretability and data processing.

Main Methods:

  • Implementation and benchmarking of four spectral model pipelines: binary classification (BreaKHis), multi-class region classification (glioblastoma), spatial transcriptomics, and denoising (Visium 10x).
  • Extensive cross-validation and grouped splits were employed.
  • Equivalence testing was used to compare spectral and CNN model performance.

Main Results:

  • Purely spectral models did not consistently improve performance over CNN-only baselines.
  • Spectral models demonstrated utility in denoising, particularly for data-scarce or heterogeneous images.
  • Fusion models combining CNNs and spectral methods showed improved balanced accuracy.
  • Spectral models exhibited poor generalization in spatial transcriptomics tasks.

Conclusions:

  • Spectral models are complementary, not superior, to CNNs for Whole Slide Image (WSI) classification or segmentation.
  • Spectral methods show promise for specific applications like denoising in challenging imaging environments.
  • Further research is needed to identify optimal conditions and data modalities for spectral model application in digital pathology.