Weakly-supervised deep learning on pathological whole-slide images for cutaneous vasculitis and its mimickers: a high-performance diagnostic support tool

Abstract

Cutaneous vasculitis represents a heterogeneous group of disorders characterized by inflammation and necrosis of blood vessels within the skin, with or without systemic involvement. The pathological diagnosis of cutaneous vasculitis remains a significant challenge due to its pathological manifestations overlapping with various inflammatory skin diseases and the inherent subjectivity of manual diagnostic criteria. This study leveraged deep learning technology to develop a reliable diagnostic model for whole slide images (WSIs) of cutaneous vasculitis, aiming to standardize and enhance the consistency of cutaneous vasculitis pathology assessment. The study incorporated WSIs from two medical research centers between 2018 and 2024, comprising 378 WSIs of cutaneous vasculitis, 285 WSIs of edematous dermatitis, 286 WSIs of granulomatous inflammation, and 247 WSIs of panniculitis. To circumvent the substantial resource burden associated with pixel-by-pixel manual annotation of ground truth labels, this study implemented a weakly-supervised learning strategy that required only pathological diagnostic reports as labels. The results demonstrated that the deep learning-based WSI diagnostic model achieved an impressive AUC of 98.39% in multi-classification diagnostic tasks, serving as a standardized adjunct to help mitigate the variability and inconsistency inherent in manual diagnosis. Furthermore, the model can generate diagnostic predictions within milliseconds and highlight regions containing highly suspicious diagnostic evidence, significantly enhancing pathologists’ work efficiency.