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UFPF: A Universal Feature Perception Framework for Microscopic Hyperspectral Images.

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    This study introduces a Universal Feature Perception Framework (UFPF) for microscopic hyperspectral imaging. The UFPF enhances diagnostic accuracy by integrating spatial and spectral features for medical applications.

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    Area of Science:

    • Medical Imaging
    • Deep Learning
    • Computational Pathology

    Background:

    • Deep learning shows promise in microscopic hyperspectral imaging diagnostics.
    • Existing models are limited to single-task/scene applications, hindering collaborative interpretation of spatial-spectral features.
    • This limits the full exploration of clinical value from hyperspectral data.

    Purpose of the Study:

    • To propose a Universal Feature Perception Framework (UFPF) for microscopic hyperspectral imaging.
    • To extract high-quality spatial-spectral features for robust downstream task performance.
    • To enhance the collaborative interpretation of hyperspectral features and spatial information.

    Main Methods:

    • Developed a hierarchical corner-to-center mamba structure with progressive focus towards the center.
    • Incorporated a dual-path spatial-spectral joint perception module for information complementarity.
    • Designed a Mamba-attention Mix-alignment for enhanced deep semantic feature alignment.

    Main Results:

    • The UFPF framework significantly improves classification and segmentation performance on multiple datasets.
    • Demonstrated enhanced feature extraction capabilities by integrating richer spatial-spectral information.
    • Validated the framework's effectiveness in supporting clinical applications of medical hyperspectral data.

    Conclusions:

    • The proposed UFPF framework offers a robust foundation for microscopic hyperspectral data analysis.
    • The innovative approach effectively integrates spatial and spectral information, boosting diagnostic potential.
    • This advancement supports the broader clinical application of medical hyperspectral imaging.