UFPF: A Universal Feature Perception Framework for Microscopic Hyperspectral Images

In recent years, deep learning has shown immense promise in advancing medical hyperspectral imaging diagnostics at the microscopic level. Despite this progress, most existing research models remain constrained to single-task or single-scene applications, lacking robust collaborative interpretation of microscopic hyperspectral features and spatial information, thereby failing to fully explore the clinical value of hyperspectral data. In this paper, we propose a microscopic hyperspectral universal feature perception framework (UFPF), which extracts high-quality spatial-spectral features of hyperspectral data, providing a robust feature foundation for downstream tasks. Specifically, this innovative framework captures different sequential spatial nearest-neighbor relationships through a hierarchical corner-to-center mamba structure. It incorporates the concept of "progressive focus towards the center", starting by emphasizing edge information and gradually refining attention from the edges towards the center. This approach effectively integrates richer spatial-spectral information, boosting the model's feature extraction capability. On this basis, a dual-path spatial-spectral joint perception module is developed to achieve the complementarity of spatial and spectral information and fully explore the potential patterns in the data. In addition, a Mamba-attention Mix-alignment is designed to enhance the optimized alignment of deep semantic features. The experimental results on multiple datasets have shown that this framework significantly improves classification and segmentation performance, supporting the clinical application of medical hyperspectral data. The code is available at: https://github.com/Qugeryolo/UFPF.