DeepLuAd: Semantic-guided virtual histopathology of lung adenocarcinoma via stimulated Raman scattering.

ABSTRACT: Rationale: Accurate histologic grading of lung adenocarcinoma is essential for guiding clinical management. Conventional hematoxylin and eosin (H&E) staining provides morphological information but lacks biochemical specificity, limiting quantitative analysis of tissue subtypes within the heterogeneous lung cancer microenvironments. Methods: We developed DeepLuAd, an AI-powered platform integrating label-free stimulated Raman scattering (SRS) microscopy with semantic-guided deep learning. The platform enables automated tumor grading, segmentation, cellular-level morpho-chemical quantification, and unsupervised virtual H&E staining. Results: DeepLuAd achieved a mean intersection-over-union (mIoU) of 80.43% across major lung tissue subtypes and reached a grading concordan

SUBMITTER: Ma L 

PROVIDER: S-EPMC12712914 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature