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Deep-learning based colorectal cancer pathological analysis with hyperspectral light field microscopy.

Hao Sheng1,2, Yingying Zhang1,2, Ruixuan Cong1,2

  • 1Key Laboratory of Data Science and Intelligent Computing, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China.

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|July 29, 2025
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Summary

This study introduces hyperspectral light field microscopy for analyzing colorectal cancer whole slide images. The novel method enhances cell classification and pathology report generation, improving diagnostic accuracy.

Keywords:
CancerData engineeringOptical imaging

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Area of Science:

  • Biomedical Imaging
  • Computational Pathology
  • Cancer Research

Background:

  • Hyperspectral light field (H-LF) microscopy captures rich 5D data by integrating multi-angular and multi-spectral information.
  • Analyzing H&E-stained whole slide images (WSIs) is crucial for colorectal cancer (CRC) diagnosis.

Purpose of the Study:

  • To develop an advanced imaging technique for detailed analysis of colorectal cancer WSIs.
  • To enable automated cell classification and pathology report generation using H-LF data.

Main Methods:

  • Integration of 5D H-LF microscopy with H&E-stained WSIs of CRC.
  • Development of a triple separable transformer encoder (HLFTST) for efficient feature extraction from H-LF data.
  • Implementation of a text encoder-decoder for aligning H-LF features with natural language for report generation.

Main Results:

  • The HLFTST method demonstrated superior performance compared to 2D, 3D, and 4D baselines.
  • Achieved improvements in precision by up to 4.88% and F1 score by 4.21% across five CRC cell categories.
  • Successfully generated meaningful pathology descriptions from H-LF data.

Conclusions:

  • The proposed H-LF microscopy approach significantly enhances the analysis of CRC WSIs.
  • This technology holds potential for improving cancer diagnostics and supporting personalized treatment strategies.
  • The method's ability to generate descriptive reports aids in clinical decision-making.