Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning

2025-03-05

AI Summary

Hematoxylin and eosin (H&E) staining, the histopathological gold standard, fails to capture critical microstructural features—such as collagen alignment and fiber orientation—that depend on birefringence and optical anisotropy. To address this limitation, we propose the first self-supervised multimodal framework integrating H&E and polarization imaging. Our method innovatively employs feature disentanglement to separate shared and modality-specific representations, enabling synergistic learning without paired annotations. We systematically demonstrate the diagnostic complementarity of polarization signals in computational pathology. Evaluated on the Chaoyang and MHIST datasets, our approach achieves 86.70% and 89.06% classification accuracy, respectively—significantly outperforming unimodal baselines and existing multimodal fusion methods. t-SNE visualization and polarized optical analysis further validate its pathological discriminability and interpretability, establishing polarization imaging as a biologically grounded, clinically actionable enhancement to conventional digital pathology workflows.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Histopathology image analysis is fundamental to digital pathology, with hematoxylin and eosin (H&E) staining as the gold standard for diagnostic and prognostic assessments. While H&E imaging effectively highlights cellular and tissue structures, it lacks sensitivity to birefringence and tissue anisotropy, which are crucial for assessing collagen organization, fiber alignment, and microstructural alterations--key indicators of tumor progression, fibrosis, and other pathological conditions. To bridge this gap, we propose PolarHE, a dual modality fusion framework that integrates H&E with polarization imaging, leveraging the polarization ability to enhance tissue characterization. Our approach employs a feature decomposition strategy to disentangle common and modality specific features, ensuring effective multimodal representation learning. Through comprehensive validation, our approach significantly outperforms previous methods, achieving an accuracy of 86.70% on the Chaoyang dataset and 89.06% on the MHIST dataset. Moreover, polarization property visualization reveals distinct optical signatures of pathological tissues, highlighting its diagnostic potential. t-SNE visualizations further confirm our model effectively captures both shared and unique modality features, reinforcing the complementary nature of polarization imaging. These results demonstrate that polarization imaging is a powerful and underutilized modality in computational pathology, enriching feature representation and improving diagnostic accuracy. PolarHE establishes a promising direction for multimodal learning, paving the way for more interpretable and generalizable pathology models. Our code will be released after paper acceptance.

Problem

• Enhances tissue characterization beyond H&E staining limitations.
• Improves diagnostic accuracy by integrating polarization imaging.
• Reveals distinct optical signatures for pathological tissue analysis.

Innovation

• PolarHE integrates H&E with polarization imaging.
• Feature decomposition disentangles common and specific features.
• Polarization imaging enhances tissue characterization and diagnostics.