PathoEye: A deep learning framework for the whole-slide image analysis of skin tissue

Objective: To provide an interpretable computational framework for examining whole-slide images (WSI) in skin biopsies, PathoEye focuses on the dermis-epidermis junctional (DEJ) areas, also known as the basement membrane zone (BMZ), to enrich the pathological features of various skin conditions. Method: We presented PathoEye for WSI analysis in dermatology, which integrates epidermis-guided sampling, deep learning and radiomics. It enables the semantic segmentation of the BMZ automatically and extracts distinct features associated with various skin conditions. Results: PathoEye outperforms the existing methods in multi-class classification tasks involving various skin conditions by leveraging the BMZ-centric segmentation approach. It enables the investigation of histopathological aberrations in aged skin compared with young skin. Additionally, it highlighted the texture changes in the BMZ of young skin compared with aged skin. Further experimental analyses revealed that senescence cells were enriched in the BMZ, and the turnover of basement membrane (BM) components, including COL17A1, COL4A2, and ITGA6, was increased in aged skin. Conclusion: PathoEye is a WSI analysis tool that focuses on the features of the BMZ related to various skin conditions. The BMZ-centric patch sampling method improves the performance of the classification model for skin diseases.