Hyperspectral imaging delivers high-resolution spectral-spatial information to support molecular tissue characterization, but its clinical utility is far from being fully realized. Existing segmentation techniques are constrained by fixed or suboptimal band selection strategies and insufficient frequency-domain modeling, which limit their ability to fully exploit discriminative spectral cues and subtle tissue structures. To address these challenges, we propose AMBS-SF2Net, a unified framework that enhances spectral representation and hierarchical frequency modeling for accurate and efficient segmentation. Specifically, the Adaptive Mask-based Band Selection (AMBS) module dynamically identifies informative spectral channels, the Adaptive Spectral-Frequency Integration (ASFI) module fuses multi-scale spatial edges and frequency-aware spectral features, and the Multi-Axis Frequency Enhanced (MAFE) module captures complementary spectral and spatial frequency patterns along different tensor dimensions. To rigorously evaluate the method’s generalizability, we conduct extensive experiments on datasets spanning distinct imaging scales, comprising a microscopic cholangiocarcinoma pathology dataset and two macroscopic tissue datasets of pig abdominal organs and human placenta. Results demonstrate that AMBS-SF2Net significantly outperforms state-of-the-art methods, exhibiting superior robustness across varying spectral resolutions and spatial modalities, thereby validating its strong potential for diverse clinical applications.

To address the core issue of high-dimensional data processing in hyperspectral pathological diagnosis, we develop a new feature selection framework using functional data analysis (FSFDA). The framework models pixel spectra as continuous functions to preserve spectral continuity, overcoming the limitations of traditional discrete representations. Based on functional features, an innovative adaptive spectral segmentation strategy driven by functional change rate is developed to achieve optimal segmentation in the feature space. Additionally, a multi-criteria scoring mechanism including supervised (FSFDA-S) and unsupervised (FSFDA-U) paradigms is developed to enhance feature diagnostic discriminability while maintaining sparsity. Experimental results on the pathological hyperspectral image dataset of membranous nephropathy validate that the proposed method achieves over 99% classification accuracy while reducing feature dimensions by 94.5%. For cross-modal data involving in-vivo human brain and white blood cells, FSFDA effectively identifies diagnostic bands aligned with histopathological signatures, verifying its adaptive feature selection ability and cross sample generalization performance.