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

ABSTRACT:

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 histopat

SUBMITTER: Lin Y 

PROVIDER: S-EPMC12699262 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature