Research on tissue section negative detection algorithm based on multispectral microscopic imaging

In recent years, the rapid advancement of artificial intelligence (AI) technology has enabled AI-assisted negative screening to significantly enhance physicians’ efficiency through image feature analysis and multimodal data modeling, allowing them to focus more on diagnosing positive cases. Meanwhile, multispectral imaging (MSI) integrates spectral and spatial resolution to capture subtle tissue features invisible to the human eye, providing high-resolution data support for pathological analysis. Combining AI technology with MSI and employing quantitative methods to analyze multiband biomarkers (such as absorbance differences in keratin pearls) can effectively improve diagnostic specificity and reduce subjective errors in manual slide interpretation. To address the challenge of identifying negative tissue sections, we developed a discrimination algorithm powered by MSI. We demonstrated its efficacy using cutaneous squamous cell carcinoma (cSCC) as a representative case study. The algorithm achieved 100% accuracy in excluding negative cases and effectively mitigated the false-positive problem caused by cSCC heterogeneity. We constructed a multispectral image (MSI) dataset acquired at 520[Formula: see text]nm, 600[Formula: see text]nm, and 630[Formula: see text]nm wavelengths. Subsequently, we employed an optimized MobileViT model for tissue classification and performed comparative analyses against other models. The experimental results showed that our optimized MobileViT model achieved superior performance in identifying negative tissue sections, with a perfect accuracy rate of 100%. Thus, our results confirm the feasibility of integrating MSI with AI to exclude negative cases with perfect accuracy, offering a novel solution to alleviate the workload of pathologists.