Automated classification of celiac disease in histopathological images: a multi-scale approach

Abstract

With a prevalence of 1-2% Celiac Disease (CD) is one of the most commonly known genetic and autoimmune diseases, which is induced by the intake of gluten in genetically predisposed persons. Diagnosing CD involves the analysis of duodenum biopsies to determine the small intestine condition. In this study, we propose a singlescale pipeline and the combination of two single-scale pipelines, forming a multi-scale approach, to accurately classify CD signs in histopathology whole slide images with automatically generated labels. The automatic classification of CD signs in histopathological images of these biopsies has not been extensively studied, resulting in the absence of a standardized guidelines or best-practices for this purpose. To fill this gap, we evaluated different magnifications and architectures, including a pre-trained MoCov2 model, for both single-and multiscale approaches. Furthermore, for the multi-scale approach, methods for aggregating feature vectors from several magnifications are explored. For the single-scale pipeline we achieved an AUC of 0.9975 and a weighted F1-score of 0.9680, while for the multiscale Pipeline an AUC of 0.9966 and a weighted F1-score of 0.9250 was achieved. On large datasets, no significant differences were observed; however, with only 10% of the dataset, the multi-scale framework outperforms the single-scale framework significantly. Moreover, the multi-scale approach requires only half of the dataset and half of the time compared to the best single-scale result to identify the optimal model. In conclusion, the multi-scale framework emerges as an exceptionally efficient solution, capable of delivering superior results with minimal data and resource demands.