Fusion and pure feature extraction framework for intraoperative hyperspectral of thyroid lesion

Thyroid cancer has remained one of the most prevalent endocrine malignancies. In routine surgery, thyroid cancer analysis involves two time-consuming steps: intraoperative frozen section preparation and manual microscopic examination. Recently, info-rich hyperspectral intelligence analysis has been studied, reducing subjective bias but only optimizing the intraoperative second step and the model complexity, ignoring the independent features that possess substance fingerprints. To bridge the gaps, we developed a hyperspectral recognition algorithm called PS4EM-SN for intraoperatively ex-vivo macro thyroid lesion, which comprised a pure spectral with pure spatial(SPS) learning framework and a spatial–spectral fusion embed mechanism(SSEM) coupled with cascade attention. The cascade attention mechanism, integrating Squeeze-and-Excitation (SE) and Non-Local (NOL) blocks, enhanced robustness to the outliers of SSEM and improved generalization. The experimental results were satisfactory in differentiating non-malignant and malignant regions with 93.91% average accuracy. Given its hyperspectral multifaceted performance, our method promises a digital solution for intraoperative thyroid diagnosis.